Method of developing a system for identifying the presence and orientation of an object in a vehicle

ABSTRACT

Method for developing and adapting a system for determining the location of an object in a passenger compartment of a vehicle using a variety of transducers and pattern recognition technologies and techniques that applies to any combination of transducers that provide information about vehicle occupancy. These include weight sensors, capacitive sensors, inductive sensors, ultrasonic, optic, infrared, radar among others. The adaptation process begins with a selection of candidate transducers for a particular vehicle model based on, e.g., cost, vehicle interior passenger compartment geometry, desired accuracy and reliability, vehicle aesthetics, vehicle manufacturer preferences. Once a candidate set of transducers is selected, these transducers are mounted in the test vehicle and the vehicle is subjected to an extensive data collection process wherein various objects are placed in the vehicle at various locations and various databases are collected. A pattern recognition system is developed using the acquired data and an accuracy assessment is made. Further studies are made to determine which if any of the transducers can be eliminated from the design. The design process usually begins with a surplus of sensors plus an objective as to how many sensors are to be in the final vehicle installation. The adaptation process can determine the degree of importance of the transducers and the least important could be eliminated to reduce system cost and complexity. Various data collection techniques are utilized such as collecting data under the influence of thermal gradients and the use of neural networks to insure data quality. Other techniques used include the use of pre-processors, post-processors, modular networks, large databases and multiple databases.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. §119(e) of U.S.provisional patent application Serial No. 60/136,163 filed May 27, 1999.

This application is continuation-in-part of U.S. patent application Ser.No. 08/919,823 filed on Aug. 28, 1997, now U.S. Pat. No. 5,943,295,which in turn is a continuation-in-part of U.S. patent application Ser.No. 08/798,029 filed Feb. 6, 1997, now abandoned.

This application is related to: (i) U.S. Pat. No. 5,653,462 entitled“Vehicle Occupant Position and Velocity Sensor” filed Jul. 21, 1995,which is a continuation of U.S. patent application Ser. No. 08/040,978filed Mar. 31, 1993, now abandoned, which in turn is a continuation ofU.S. patent application Ser. No. 07/878,571 filed May 5, 1992, nowabandoned; (ii) U.S. Pat. No. 5,829,782 entitled “Vehicle InteriorIdentification and Monitoring System” filed May 9, 1994; (iii) U.S. Pat.No. 5,845,000 entitled “Optical Identification and Monitoring SystemUsing Pattern Recognition for Use with Vehicles” filed Jun. 7, 1995;(iv) U.S. Pat. No. 5,822,707 entitled “Automatic Vehicle Seat Adjuster”filed Jun. 7, 1995; (v) U.S. Pat. No. 5,748,473 entitled “AutomaticVehicle Seat Adjuster” filed Jun. 7, 1995; and, (vi) U.S. Pat. No.5,835,613 entitled “Optical Identification and Monitoring System UsingPattern Recognition for use with Vehicles” filed Jun. 7, 1995, which areall incorporated by reference herein.

FIELD OF THE INVENTION

The present invention relates generally to the fields of sensing,detecting, monitoring and/or identifying various objects, and partsthereof, which are located within the passenger compartment of a motorvehicle. In particular, the present invention relates to an efficientand highly reliable method for developing a system for detecting theorientation of an object in the passenger compartment, e.g., a rearfacing child seat (RFCS) situated in the passenger compartment in alocation where it may interact with a deploying occupant protectionapparatus, such as an airbag, and/or for detecting an out-of-positionoccupant. The resulting system permits the control and selectivesuppression of deployment of the occupant protection apparatus when thedeployment may result in greater injury to the occupant than the crashforces themselves. This is accomplished in part through a method ofdetermining the placement of transducers of the system, a method ofdeveloping a pattern recognition system including a method of training aneural network and/or a method for developing a system for the novelanalysis of the signals from the transducers.

The application of the occupant position sensor to a new automobilevehicle model is called applications engineering. Applicationsengineering of occupant sensors comprises, inter alia, determining thelocation of the transducers, designing the transducer holders,determining the wiring layout, performing a tolerance study on thetransducer locations and angular orientation, designing the circuits forthe particular vehicle model, interfacing or integrating the circuitsinto the vehicle electronic system, and adapting the occupant sensorsystem to the particular vehicle model.

All of the above aspects of application engineering, with the exceptionof the system adaptation, are standard processes that do not differsignificantly from the application engineering of any electronic systemto a new vehicle model. The system adaptation, however, is unique inthat it requires considerable skill and expertise and the use of noveltechnologies to create a system that is optimized for a particularvehicle.

BACKGROUND OF THE INVENTION

1. Prior Art on Sensing of Out-of-position Occupants and Rear FacingChild Seats

Whereas thousands of lives have been saved by airbags, a large number ofpeople have also been injured, some seriously, by the deploying airbag,and thus significant improvements to the airbag system are necessary. Asdiscussed in detail in one or more of the patents and patentapplications cross-referenced above, for a variety of reasons, vehicleoccupants may be too close to the airbag before it deploys and can beseriously injured or killed as a result of any deployment thereof Also,a child in a rear facing child seat which is placed on the right frontpassenger seat is in danger of being seriously injured if the passengerairbag deploys. For these reasons and, as first publicly disclosed inBreed, D. S. “How Airbags Work” presented at the InternationalConference on Seatbelts and Airbags in 1993, in Canada, occupantposition sensing and rear facing child seat detection is required inorder to minimize the damages caused by deploying airbags. It is also berequired in order to minimize the damage caused by the deployment ofother types of occupant protection and/or restraint devices which mightbe installed in the vehicle.

Initially, these systems will solve the out-of-position occupant and therear facing child seat problems related to current airbag systems andprevent unneeded and unwanted airbag deployments when a front seat isunoccupied. However, airbags are now under development to protect rearseat occupants in vehicle crashes and all occupants in side impacts. Asystem is therefore needed to detect the presence of occupants,determine if they are out-of-position (defined below) and to identifythe presence of a rear facing child seat in the rear seat. Futureautomobiles are expected to have eight or more airbags as protection issought for rear seat occupants and from side impacts. In addition toeliminating the disturbance and possible harm of unnecessary airbagdeployments, the cost of replacing these airbags will be excessive ifthey all deploy in an accident needlessly.

Inflators now exist which will adjust the amount of gas flowing to orfrom the airbag to account for the size and position of the occupant andfor the severity of the accident. The vehicle identification andmonitoring system (VIMS) discussed in U.S. Pat. No. 5,829,782, and U.S.patent application Ser. No. 08/798,029 filed Feb. 6, 1997 among others,will control such inflators based on the presence and position ofvehicle occupants or of a rear facing child seat. The instant inventionis concerned with the process of adapting the vehicle interiormonitoring systems to a particular vehicle model and achieving a highsystem accuracy and reliability as discussed in greater detail below.

The automatic adjustment of the deployment rate of the airbag based onoccupant identification and position and on crash severity has beentermed “smart airbags”. Central to the development of smart airbags isthe occupant identification and position determination systems describedin the above-referenced patents and patent applications and to themethods described herein for adapting those systems to a particularvehicle model. To complete the development of smart airbags, ananticipatory crash detecting system such as disclosed in U.S. patentapplication Ser. No. 08/247,760 filed May 23, 1994 is also desirable.Prior to the implementation of anticipatory crash sensing, the use of aneural network smart crash sensor which identifies the type of crash andthus its severity based on the early part of the crash accelerationsignature should be developed and thereafter implemented. U.S. Pat. No.5,684,701 (Breed) describes a crash sensor based on neural networks.This crash sensor, as with all other crash sensors, determines whetheror not the crash is of sufficient severity to require deployment of theairbag and, if so, initiates the deployment. A neural network based on asmart airbag crash sensor could also be designed to identify the crashand categorize it with regard to severity thus permitting the airbagdeployment to be matched not only to the characteristics and position ofthe occupant but also the severity and timing of the crash itself (thisbeing described in U.S. patent application Ser. No. 08/798,029referenced above).

The need for an occupant out-of-position sensor has also been observedby others and several methods have been described in certain U.S.patents for determining the position of an occupant of a motor vehicle.However, no patents have been found that describe the methods ofadapting such sensors to a particular vehicle model to obtain highsystem accuracy. Each of these systems will be discussed below and havesignificant limitations.

In White et al. (U.S. Pat. No. 5,071,160), for example, a singleacoustic sensor and detector is described and, as illustrated, ismounted lower than the steering wheel. White et al. correctly perceivethat such a sensor could be defeated, and the airbag falsely deployed,by an occupant adjusting the control knobs on the radio and thus theysuggest the use of a plurality of such sensors. White et al. does notdisclose where the such sensors would be mounted, other than on theinstrument panel below the steering wheel, or how they would be combinedto uniquely monitor particular locations in the passenger compartmentand to identify the object(s) occupying those locations. The adaptationprocess to vehicles is not described.

Mattes et al. (U.S. Pat. No. 5,118,134) describe a variety of methodsfor measuring the change in position of an occupant includingultrasonic, active or passive infrared radiation and microwave radarsensors, and an electric eye. The use of these sensors is to measure thechange in position of an occupant during a crash and they use thatinformation to assess the severity of the crash and thereby decidewhether or not to deploy the airbag. They are thus using the occupantmotion as a crash sensor. No mention is made of determining theout-of-position status of the occupant or of any of the other featuresof occupant monitoring as disclosed in the above-referenced patentsand/or patent applications. It is interesting to note that nowhere doesMattes et al. discuss how to use a combination of ultrasonicsensors/transmitters to identify the presence of a human occupant andthen to find his/her location in the passenger compartment.

The object of an occupant out-of-position sensor is to determine thelocation of, e.g., the head and/or chest of the vehicle occupant in thepassenger compartment to enable the location of the head and/or chest tobe determined relative to the occupant protection apparatus, e.g.,airbag, since it is the impact of either the head or chest with thedeploying airbag which can result in serious injuries. Both White et al.and Mattes et al. disclose only lower mounting locations of theirsensors which are mounted in front of the occupant such as on thedashboard or below the steering wheel. Both such mounting locations areparticularly prone to detection errors due to positioning of theoccupant's hands, arms and legs. This would require at least three, andpreferably more, such sensors and detectors and an appropriate logiccircuitry which ignores readings from some sensors if such readings areinconsistent with others, for the case, for example, where the driver'sarms are the closest objects to two of the sensors. The determination ofthe proper transducer mounting locations, aiming and field angles for aparticular vehicle model are not disclosed in either White et al. orMattes et al. and are part of the vehicle model adaptation processdescribed herein.

White et al. also describe the use of error correction circuitry,without defining or illustrating the circuitry, to differentiate betweenthe velocity of one of the occupant's hands, as in the case where he/sheis adjusting the knob on the radio, and the remainder of the occupant.Three ultrasonic sensors of the type disclosed by White et al. might, insome cases, accomplish this differentiation if two of them indicatedthat the occupant was not moving while the third was indicating that heor she was moving. Such a combination, however, would not differentiatebetween an occupant with both hands and arms in the path of theultrasonic transmitter at such a location that they were blocking asubstantial view of the occupant's head or chest. Since the sizes anddriving positions of occupants are extremely varied, trained patternrecognition systems, such as neural networks, are required when a clearview of the occupant, unimpeded by his/her extremities, cannot beguaranteed. White et al. do not suggest the use of such neural networks.

Fujita et al., in U.S. Pat. No. 5,074,583, describe another method ofdetermining the position of the occupant but do not use this informationto control and suppress deployment of an airbag if the occupant isout-of-position, or if a rear facing child seat is present. In fact, thecloser that the occupant gets to the airbag, the faster the inflationrate of the airbag is according to the Fujita et al. patent, whichthereby increases the possibility of injuring the occupant. Fujita etal. do not measure the occupant directly but instead determine his orher position indirectly from measurements of the seat position and thevertical size of the occupant relative to the seat. This occupant heightis determined using an ultrasonic displacement sensor mounted directlyabove the occupant's head.

It is important to note that in all cases in the above-cited prior art,except those assigned to the current assignee of the instant invention,no mention is made of the method of determining transducer location,deriving the algorithms or other system parameters that allow the systemto accurately identify and locate an object in the vehicle. In contrast,in one implementation of the instant invention, the return ultrasonicecho pattern over several milliseconds corresponding to the entireportion of the passenger compartment volume of interest is analyzed frommultiple transducers and sometimes combined with the output from othertransducers, providing distance information to many points on the itemsoccupying the passenger compartment.

Many of the teachings of this invention are based on pattern recognitiontechnologies as taught in numerous textbooks and technical papers.Central to the diagnostic teachings of this invention is the manner inwhich the diagnostic module determines a normal pattern from an abnormalpattern and the manner in which it decides what data to use from thevast amount of data available. This is accomplished using patternrecognition technologies, such as artificial neural networks, andtraining. The theory of neural networks including many examples can befound in several books on the subject including: Techniques AndApplication Of Neural Networks, edited by Taylor, M. and Lisboa, P.,Ellis Horwood, West Sussex, England, 1993; Naturally IntelligentSystems, by Caudill, M. and Butler, C., MIT Press, Cambridge Mass.,1990; J. M. Zaruda, Introduction to Artificial Neural Systems, Westpublishing Co., N.Y., 1992 and, Digital Neural Networks, by Kung, S. Y.,PTR Prentice Hall, Englewood Cliffs, N.J., 1993, Eberhart, R., Simpson,P. and Dobbins, R., Computational Intelligence PC Tools, Academic Press,Inc., 1996, Orlando, Fla., all of which are included herein byreference. The neural network pattern recognition technology is one ofthe most developed of pattern recognition technologies.

2. Definitions

The use of pattern recognition, or more particularly how it is used, iscentral to the instant invention. In the above-cited prior art, exceptin that assigned to the current assignee of the instant invention,pattern recognition which is based on training, as exemplified throughthe use of neural networks, is not mentioned for use in monitoring theinterior passenger compartment or exterior environments of the vehicle.Thus, the methods used to adapt such systems to a vehicle are also notmentioned.

“Pattern recognition” as used herein will generally mean any systemwhich processes a signal that is generated by an object (e.g.,representative of a pattern of returned or received impulses, waves orother physical property specific to and/or characteristic of and/orrepresentative of that object) or is modified by interacting with anobject, in order to determine to which one of a set of classes that theobject belongs. Such a system might determine only that the object is oris not a member of one specified class, or it might attempt to assignthe object to one of a larger set of specified classes, or find that itis not a member of any of the classes in the set. The signalsprocessed'are generally a series of electrical signals coming fromtransducers that are sensitive to acoustic (ultrasonic) orelectromagnetic radiation (e.g., visible light or infrared radiation),although other sources of information are frequently included.

A trainable or a trained pattern recognition system as used hereingenerally means a pattern recognition system which is taught torecognize various patterns constituted within the signals by subjectingthe system to a variety of examples. The most successful such system isthe neural network. Thus, to generate the pattern recognition algorithm,test data is first obtained which constitutes a plurality of sets ofreturned waves, or wave patterns, from an object (or from the space inwhich the object will be situated in the passenger compartment, i.e.,the space above the seat) and an indication of the identify of thatobject, (e.g., a number of different objects are tested to obtain theunique wave patterns from each object). As such, the algorithm isgenerated, and stored in a computer processor, and which can later beapplied to provide the identity of an object based on the wave patternbeing received during use by a receiver connected to the processor andother information. For the purposes here, the identity of an objectsometimes applies to not only the object itself but also to its locationand/or orientation in the passenger compartment. For example, a rearfacing child seat is a different object than a forward facing child seatand an out-of-position adult is a different object than a normallyseated adult.

To “identify” as used herein will generally mean to determine that theobject belongs to a particular set or class. The class may be onecontaining, for example, all rear facing child seats, one containing allhuman occupants, or all human occupants not sitting in a rear facingchild seat depending on the purpose of the system. In the case where aparticular person is to be recognized, the set or class will containonly a single element, i.e., the person to be recognized.

An “occupying item” of a seat may be a living occupant such as a humanor a dog, another living organism such as a plant, or an inanimateobject such as a box or bag of groceries or an empty child seat.

“Out-of-position” as used for an occupant will generally means that theoccupant, either the driver or a passenger, is sufficiently close to theoccupant protection apparatus (airbag) prior to deployment that he orshe is likely to be more seriously injured by the deployment eventitself than by the accident. It may also mean that the occupant is notpositioned appropriately in order to attain beneficial, restrainingeffects of the deployment of the airbag. As for the occupant being tooclose to the airbag, this typically occurs when the occupant's head orchest is closer than some distance such as about 5 inches from thedeployment door of the airbag module. The actual distance value whereairbag deployment should be suppressed depends on the design of theairbag module and is typically farther for the passenger airbag than forthe driver airbag.

“Transducer” as used herein will generally mean the combination of atransmitter and a receiver. In come cases, the same device will serveboth as the transmitter and receiver while in others two separatedevices adjacent to each other will be used. In some cases, atransmitter is not used and in such cases transducer will mean only areceiver. Transducers include, for example, capacitive, inductive,ultrasonic, electromagnetic (antenna, CCD, CMOS arrays), weightmeasuring or sensing devices.

“Adaptation” as used here represents the method by which a particularoccupant sensing system is designed:and arranged for a particularvehicle model. It includes such things as the process by which thenumber, kind and location of various transducers is determined. Forpattern recognition systems, it includes the process by which thepattern recognition system is taught to recognize the desired patterns.In this connection, it will usually include (1) the method of training,(2) the makeup of the databases used for training, testing andvalidating the particular system, or, in the case of a neural network,the particular network architecture chosen, (3) the process by whichenvironmental influences are incorporated into the system, and (4) anyprocess for determining the pre-processing of the data or the postprocessing of the results of the pattern recognition system. The abovelist is illustrative and not exhaustive. Basically, adaptation includesall of the steps that are undertaken to adapt transducers and othersources of information to a particular vehicle to create the systemwhich accurately identifies and determines the location of an occupantor other object in a vehicle.

In the description herein on anticipatory sensing, the term“approaching” when used in connection with the mention of an object orvehicle approaching another will generally mean the relative motion ofthe object toward the vehicle having the anticipatory sensor system.Thus, in a side impact with a tree, the tree will be considered asapproaching the side of the vehicle and impacting the vehicle. In otherwords, the coordinate system used in general will be a coordinate systemresiding in the target vehicle. The “target” vehicle is the vehiclewhich is being impacted. This convention permits a general descriptionto cover all of the cases such as where (i) a moving vehicle impactsinto the side of a stationary vehicle, (ii) where both vehicles aremoving when they impact, or (iii) where a vehicle is moving sidewaysinto a stationary vehicle, tree or wall.

3. Pattern Recognition Prior Art

Japanese Patent 3-42337 (A) to Ueno describes a device for detecting thedriving condition of a vehicle driver comprising a light emitter forirradiating the face of the driver and a means for picking up the imageof the driver and storing it for later analysis. Means are provided forlocating the eyes of the driver and then the irises of the eyes and thendetermining if the driver is looking to the side or sleeping. Uenodetermines the state of the eyes of the occupant rather. thandetermining the location of the eyes relative to the other parts of thevehicle passenger compartment. Such a system can be defeated if thedriver is wearing glasses, particularly sunglasses, or another opticaldevice which obstructs a clear view of his/her eyes. Pattern recognitiontechnologies such as neural networks are not used. The method of findingthe eyes is described but not a method of adapting the system to aparticular vehicle model.

U.S. Pat. No. 5,008,946 to Ando uses a complicated set of rules toisolate the eyes and mouth of a driver and uses this information topermit the driver to control the radio, for example, or other systemswithin the vehicle by moving his eyes and/or mouth. Ando uses naturallight and illuminates only the head of the driver. He also makes no useof trainable pattern recognition systems such as neural networks, nor isthere any attempt to identify the contents of the vehicle nor of theirlocation relative to the vehicle passenger compartment. Rather, Ando islimited to control of vehicle devices by responding to motion of thedriver's mouth and eyes. As with Ueno, a method of finding the eyes isdescribed but not a method of adapting the system to a particularvehicle model.

U.S. Pat. No. 5,298,732 to Chen also concentrates in locating the eyesof the driver so as to position a light filter between a light sourcesuch as the sun or the lights of an oncoming vehicle, and the driver'seyes. Chen does not explain in detail how the eyes are located but doessupply a calibration system whereby the driver can adjust the filter sothat it is at the proper position relative to his or her eyes. Chenreferences the use of an automatic equipment for determining thelocation of the eyes but does not describe how this equipment works. Inany event, in Chen, there is no mention of monitoring the position ofthe occupant, other that the eyes, determining the position of the eyesrelative to the passenger compartment, or identifying any other objectin the vehicle other than the driver's eyes. Also, there is no mentionof the use of a trainable pattern recognition system. A method forfinding the eyes is described but not a method of adapting the system toa particular vehicle model.

U.S. Pat. No. 5,305,012 to Faris also describes a system for reducingthe glare from the headlights of an oncoming vehicle. Faris locates theeyes of the occupant by using two spaced apart infrared cameras usingpassive infrared radiation from the eyes of the driver. Faris is onlyinterested in locating the driver's eyes relative to the sun or oncomingheadlights and does not identify or monitor the occupant or locate theoccupant, a rear facing child seat or any other object for that matter,relative to the passenger compartment or the airbag. Also, Faris doesnot use trainable pattern recognition techniques such as neuralnetworks. Faris, in fact, does not even say how the eyes of the occupantare located but refers the reader to a book entitled Robot Vision (1991)by Berthold Horn, published by MIT Press, Cambridge, Mass. Also, Farisuses the passive infrared radiation rather than illuminating theoccupant with ultrasonic or electromagnetic radiation as in someimplementations of the instant invention. A method for finding the eyesof the occupant is described but not a method of adapting the system toa particular vehicle model.

The use of neural networks as the pattern recognition technology and themethods of adapting this to a particular vehicle, such as the trainingmethods, is important to this invention since it makes the monitoringsystem robust, reliable and accurate. The resulting algorithm created bythe neural network program is usually only a few hundred lines of codewritten in the C computer language and is in general fewer lines thanwhen the techniques of the above patents to Ando, Chen and Faris areimplemented. As a result, the resulting systems are easy to implement ata low cost making them practical for automotive applications. The costof the ultrasonic transducers, for example, is expected to be less thanabout $1 in quantities of one million per year. Similarly, theimplementation of the techniques of the above-referenced patentsrequires expensive microprocessors while the implementation with neuralnetworks and similar trainable pattern recognition technologies permitsthe use of low cost microprocessors typically costing less than about $5in quantities of one million per year.

The present invention uses sophisticated software that developstrainable pattern recognition algorithms such as neural networks.Usually the data is preprocessed, as discussed below, using variousfeature extraction techniques and the results post-processed to improvesystem accuracy. A non-automotive example of such a pattern recognitionsystem using neural networks on sonar signals is discussed in two papersby Gorman, R. P. and Sejnowski, T. J. “Analysis of Hidden Units in aLayered Network Trained to Classify Sonar Targets”, Neural Networks,Vol. 1. pp. 75-89, 1988, and “Learned Classification of Sonar TargetsUsing a Massively Parallel Network”, IEEE Transactions on Acoustics,Speech, and Signal Processing, Vol. 36, No. 7, July 1988. Examples offeature extraction techniques can be found in U.S. Pat. No. 4,906,940entitled “Process and Apparatus for the Automatic Detection andExtraction of Features in Images and Displays” to Green et al. Examplesof other more advanced and efficient pattern recognition techniques canbe found in U.S. Pat. No. 5,390,136 entitled “Artificial Neuron andMethod of Using Same and U.S. patent application Ser. No. 08/076,601entitled “Neural Network and Method of Using Same” to Wang, S. T. Otherexamples include U.S. Pat. No. 5,235,339 (Morrison et al.), U.S. Pat.No. 5,214,744 (Schweizer et al), U.S. Pat. No. 5,181,254 (Schweizer etal), and U.S. Pat. No. 4,881,270 (Knecht et al). All of the referencesherein are included herein by reference.

4. Ultrasonics and Optics

Both laser and non-laser optical systems in general are good atdetermining the location of objects within the two dimensional plane ofthe image and a pulsed laser radar system in the scanning mode candetermine the distance of each part of the image from the receiver bymeasuring the time of flight through range gating techniques. It is alsopossible to determine distance with the non-laser system by focusing, orstereographically if two spaced apart receivers are used and, in somecases, the mere location in the field of view can be used to estimatethe position relative to the airbag, for example. Finally, a recentlydeveloped pulsed quantum well diode laser also provides inexpensivedistance measurements as discussed in U.S. provisional patentapplication Serial No. 60/114,507, filed Dec. 31, 1998, which isincluded herein by reference as if the entire contents were copied here.

Acoustic systems are additionally quite effective at distancemeasurements since the relatively low speed of sound permits simpleelectronic circuits to be designed and minimal microprocessor capabilityis required. If a coordinate system is used where the z axis is from thetransducer to the occupant, acoustics are good at measuring z dimensionswhile simple optical systems using a single CCD or CMOS arrays are goodat measuring x and y dimensions. The combination of acoustics andoptics, therefore, permits all three measurements to be made from onelocation with low cost components as discussed in commonly assigned U.S.Pat. Nos. 5,845,000 and 5,835,613 cross-referenced above.

One example of a system using these ideas is an optical system whichfloods the passenger seat with infrared light coupled with a lens and areceiver array, e.g., CCD or CMOS array, which receives and displays thereflected light and an analog to digital converter (ADC) which digitizesthe output of the CCD or CMOS and feeds it to an Artificial NeuralNetwork (ANN) or other pattern recognition system for analysis. Thissystem uses an ultrasonic transmitter and receiver for measuring thedistances to the objects located in the passenger scat. The receivingtransducer feeds its data into an ADC and from there the converted datais directed into the ANN. The same ANN can be used for both systemsthereby providing full three-dimensional data for the ANN to analyze.This system, using low cost components, will permit accurateidentification and distance measurements not possible by either systemacting alone. If a phased array system is added to the acoustic part ofthe system, the optical part can determine the location of the driver'sears, for example, and the phased array can direct a narrow beam to thelocation and determine the distance to the occupant's ears.

Although the use of ultrasound for distance measurement has manyadvantages, it also has some drawbacks. First, the speed of sound limitsthe rate at which the position of the occupant can be updated toapproximately 10 milliseconds, which though sufficient for most cases,is marginal if the position of the occupant is to be tracked during avehicle crash. Second, ultrasound waves are diffracted by changes in airdensity that can occur when the heater or air conditioner is operated orwhen there is a high-speed flow of air past the transducer. Third, theresolution of ultrasound is limited by its wavelength and by thetransducers, which are high Q tuned devices. Typically, the resolutionof ultrasound is on the order of about 2 to 3 inches. Finally, thefields from ultrasonic transducers are difficult to control so thatreflections from unwanted objects or surfaces add noise to the data.

Ultrasonics alone can be used in several configurations for monitoringthe interior of a passenger compartment of an automobile as described inthe above-referenced patents and patent applications and in particularin U.S. patent application Ser. No. 08/798,029. Using the teachings ofthis invention, the optimum number and location of the ultrasonic and/oroptical transducers can be determined as part of the adaptation processfor a particular vehicle model.

In the cases of the instant invention, as discussed in more detailbelow, regardless of the number of transducers used, a trained patternrecognition system, as defined above, is used to identify and classify,and in some cases to locate, the illuminated object and its constituentparts.

5. Applications

The applications for this technology are numerous as described in thepatents and patent applications listed above. However, the main focus ofthe instant invention is the process of adapting the system in thepatents and patent applications referenced above for the detection ofthe presence of an occupied child seat in the rear facing position or anout-of-position occupant and the detection of an occupant in a normalseating position. The system is designed so that in the former twocases, deployment of the occupant protection apparatus (airbag) may becontrolled and possibly suppressed and in the latter, it will becontrolled and enabled.

One preferred implementation of a first generation occupant sensingsystem, which is adapted to various vehicle models using the teachingspresented herein, is an ultrasonic occupant position sensor. This systemuses an Artificial Neural Network (ANN) to recognize patterns that ithas been trained to identify as either airbag enable or airbag disableconditions. The pattern is obtained from four ultrasonic transducersthat cover the front passenger seating area. This pattern consists ofthe ultrasonic echoes bouncing off of the objects in the passenger seatarea. The signal from each of the four transducers consists of theelectrical image of the return echoes, which is processed by theelectronics. The electronic processing comprises amplification,logarithmic compression, rectification, and demodulation (band passfiltering), followed by discretization (sampling) and digitization ofthe signal. The only software processing required, before this signalcan be fed into the artificial neural network, is normalization (i.e.,mapping the input to numbers between 0 and 1). Although this is a fairamount of processing, the resulting signal is still considered “raw”,because all information is treated equally.

OBJECTS AND SUMMARY OF THE INVENTION

In general, it is an object of the present invention to provide a newand improved method for developing a system for identifying thepresence, position and orientation of an object in a vehicle.

It is another broad object of the present invention to provide a methodfor developing a system for accurately detecting the presence of anoccupied rear-facing child seat in order to prevent an occupantprotection apparatus such as an airbag from deploying, when the airbagwould impact against the rear-facing child seat if deployed.

It is yet another broad object of the present invention to provide amethod for developing a system for accurately detecting the presence ofan out-of-position occupant in order to prevent one or more deployableoccupant protection apparatus such as airbags from deploying when theairbag(s) would impact against the head or chest of the occupant duringits initial deployment phase causing injury or possible death to theoccupant.

This invention is a method to develop and adapt a system to identify,locate and monitor occupants, including their parts, and other objectsin the passenger compartment and in particular an occupied child seat inthe rear facing position or an out-of-position occupant, by illuminatingthe contents of the vehicle with ultrasonic or electromagneticradiation, for example, by transmitting radiation waves from a wavegenerating apparatus into a space above the seat, and receivingradiation modified by passing through the space above the seat using twoor more transducers properly located in the vehicle passengercompartment, in specific predetermined optimum location. Moreparticularly, this invention relates to a method for appropriatelylocating and mounting the transducers and for analyzing the receivedradiation from any object which modifies the waves, in order to achievean accuracy of recognition heretofore not possible. Outputs from thereceivers, are analyzed by appropriate computational means employingtrained pattern recognition technologies, to classify, identify and/orlocate the contents, and/or determine the orientation of, for example, arear facing child seat. In general, the information obtained by theidentification and monitoring system is used to affect the operation ofsome other system, component or device in the vehicle and particularlythe passenger and/or driver airbag systems, which may include a frontairbag, a side airbag, a knee bolster, or combinations of the same.However, the information obtained can be used for a multitude of othervehicle systems.

When the vehicle interior monitoring system developed using theteachings of this invention is installed in the passenger compartment ofan automotive vehicle equipped with a occupant protection apparatus,such as an inflatable airbag, and the vehicle is subjected to a crash ofsufficient severity that the crash sensor has determined that theprotection apparatus is to be deployed, the system, developed inaccordance with the invention, has determined prior to the deploymentwhether a child placed in the rear facing position in the child seat ispresent and if so, a signal has been sent to the control circuitry thatthe airbag should be controlled and most likely disabled and notdeployed in the crash. It must be understood though that instead ofsuppressing deployment, it is possible that the deployment may becontrolled so that it might provide some meaningful protection for theoccupied rear-facing child seat. The system developed using theteachings of this invention also determines the position of the vehicleoccupant relative to the airbag and controls and possibly disablesdeployment of the airbag if the occupant is positioned so that he/she islikely to be injured by the deployment of the airbag. As before, thedeployment is not necessarily disabled but may be controlled to provideprotection for the out-of-position occupant.

Principle objects and advantages of the methods in accordance with theinvention are:

1. To provide a reliable method for developing and adapting a system forrecognizing the presence of a rear-facing child seat on a particularseat of a motor vehicle.

2. To provide a reliable method for developing and adapting a system forrecognizing the presence of a human being on a particular seat of amotor vehicle.

3. To provide a reliable method for developing and adapting a system fordetermining the position, velocity or size of an occupant in a motorvehicle.

4. To provide a reliable method for developing and adapting a system fordetermining in a timely manner that an occupant is out-of-position, orwill become out-of-position, and likely to be injured by a deployingairbag.

5. To provide a method for locating transducers within the passengercompartment at specific locations such that a high reliability ofclassification of objects and their position is obtained from thesignals generated by the transducers.

6. To provide a method for combining a variety of transducers includingseatbelt payout sensors, seatbelt buckle sensors, seat position sensors,seatback position sensors, and weight sensors into a system and adaptthat system so as to provide a highly reliable occupant presence andposition system when used in combination with electromagnetic,ultrasonic or other radiation sensors.

Accordingly, a first embodiment of a method of developing a system fordetermining the occupancy state of a seat in a passenger compartment ofa vehicle comprises the steps of mounting transducers in the vehicle,which transducers would be affected by the occupancy state of the seat,forming at least one database comprising multiple data sets, each dataset representing a different occupancy state of the seat and beingformed by receiving data from the transducers while the seat is in thatoccupancy state, and processing the data received from the transducers,and creating a first algorithm from the database(s) capable of producingan output indicative of the occupancy state of the seat upon inputting anew data set representing an occupancy state of the seat. The new dataset would be formed, e.g., during use of the vehicle after the algorithmis installed in the control circuitry of the vehicle. The firstalgorithm may be created by inputting the database(s) into analgorithm-generating program, and running the algorithm-generatingprogram to produce the first algorithm. The first algorithm could be aneural network algorithm, in which case, the back propagation methodcould be used when generating the neural network algorithm.

The occupancy states of the seat include occupancy of the seat by anobject selected from the group comprising occupied and unoccupied rearfacing infant seats, forward facing humans, out-of-position humans,occupied and unoccupied forward facing child seats and empty seats. Theoccupancy states of the seat should also include occupancy by theobjects in multiple orientations and/or having at least one accessoryselected from a non-exclusive group comprising newspapers, books, maps,bottles, toys, hats, coats, boxes, bags and blankets.

The data can be pre-processed prior to being formed into the data sets.This may entail using data created from features of the data in the dataset, which features might be selected from a group comprising thenormalization factor, the number of data points prior to a peak, thetotal number of peaks, and the mean or variance of the data set. Also,the data sets could be mathematically transformed using normalization,truncation, logarithmic transformation, sigmoid transformation,thresholding, averaging the data over time, Fourier transforms and/orwavelet transforms. Further, pre-processing could entail subtractingdata in one data set from the corresponding data in another data set tocreate a third data set of differential data.

The processing step may comprise the step of converting the analog datafrom the transducers to digital data and combining the digital data froma plurality of the transducers to form a vector comprising a string ofdata from each of the transducers. As such, the first algorithm iscreated such that upon inputting a vector from a new data set willproduce an output representing the occupancy state of the vehicle seat.The vectors in the database can be normalized so that all values of thedata that comprise each vector are between a maximum and a minimum.

Another method of developing a system for determining the occupancystate of the vehicle seat in the passenger compartment of a vehiclecomprises the steps of forming data sets by obtaining datarepresentative of various occupying objects at various positions in thepassenger compartment and operating on at least a portion of the data toreduce the magnitude of the largest data values in a data set relativeto the smallest data values, forming a database comprising multiple datasets, and creating an algorithm from the database capable of producingan output indicative of the occupancy state of the vehicle seat uponinputting a data set representing an occupancy state of the seat.Operating on the data may entail using an approximate logarithmictransformation function.

A method of developing a database for use in developing a system fordetermining the occupancy state of a vehicle seat in accordance with theinvention comprises the steps of mounting transducers in the vehicle andwhich would be affected by the occupancy state of the seat, providingthe seat with an initial occupancy state, receiving data from thetransducers, processing the data from the transducers to form a data setrepresentative of the initial occupancy state of the vehicle seat,changing the occupancy state of the seat and repeating the datacollection process to form another data set, collecting at least 1000data sets into a first database, each representing a different occupancystate of the seat and creating an algorithm from the first databasewhich correctly identifies the occupancy state of the seat for most ofthe data sets in the first database. The algorithm is tested using asecond database of data sets which were not used in the creation of thealgorithm. The occupancy states in the second database are which werenot correctly identified by the algorithm are identified and new datacomprising similar occupancy states to the incorrectly identified statesis collected. The new data is combined with the first database, a newalgorithm is created based on the combined database and this process isrepeated until the desired accuracy of the algorithm is achieved.

Another method of developing a system for determining the occupancystate of a passenger compartment seat of a vehicle comprises the stepsof mounting a plurality of ultrasonic transducers in the vehicle (whichtransducers would be affected by the occupancy state of the seat),receiving an analog signal from each of the transducers, processing theanalog signals from the transducers to form a data set comprisingmultiple data values from each transducer representative of theoccupancy state of the vehicle, said data processing comprising thesteps of demodulation, sampling and digitizing of the transducer data tocreate a data set of digital data, forming a database comprisingmultiple data sets and creating at least one algorithm from the databasecapable of producing an output indicative of the occupancy state of theseat upon inputting a new data set representing an occupancy state ofthe seat.

Still another method of developing a system for determining theoccupancy state of a vehicle seat in a passenger compartment of avehicle comprises the steps of mounting a set of transducers on thevehicle, receiving data from the transducers, processing the data fromtransducers to form a data set representative of the occupancy state ofthe vehicle, forming a database comprising multiple data sets, creatingan algorithm from the database capable of producing an output indicativeof the occupancy state of the vehicle seat upon inputting a new dataset, and developing a measure of system accuracy. At least onetransducer is removed from the transducer set, a new database is createdcontaining data only from the reduced number of transducers, a newalgorithm is developed based on the new database and tested to determinethe new system accuracy. The process of removing transducers, algorithmdevelopment and testing is continued until the minimum number of sensorsis determined which produces an algorithm having desired accuracy. Thetransducers are selected from a group consisting of ultrasonictransducers, optical sensors, capacitive sensors, weight sensors, seatposition sensors, seatback position sensors, seat belt buckle sensors,seatbelt payout sensors, infrared sensors, inductive sensors and radarsensors.

Yet another method of developing a system for determining the occupancystate of the driver and passenger seats of a vehicle comprises the stepsof mounting ultrasonic transducers having different transmitting andreceiving frequencies in a vehicle such that transducers having adjacentfrequencies are not within the direct ultrasonic field of each other,receiving data from the transducers, processing the data from thetransducers to form a data set representative of the occupancy state ofthe vehicle, forming at least one database comprising multiple data setsand creating at least one algorithm from the at least one databasecapable of producing an output indicative of the occupancy state of avehicle seat upon inputting a new data set.

These and other objects and advantages will become apparent from thefollowing description of the preferred embodiments of the vehicleidentification and monitoring system of this invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawings are illustrative of embodiments of the systemdeveloped or adapted using the teachings of this invention and are notmeant to limit the scope of the invention as encompassed by the claims.In particular, the illustrations below are limited to the monitoring ofthe front passenger seat for the purpose of describing the system.Naturally, the invention applies as well to adapting the system to theother seating positions in the vehicle and particularly to the driverposition.

FIG. 1 shows a seated-state detecting unit developed in accordance withthe present invention and the connections between ultrasonic orelectromagnetic sensors, a weight sensor, a reclining angle detectingsensor, a seat track position detecting sensor, a heartbeat sensor, amotion sensor, a neural network circuit, and an airbag system installedwithin a vehicle compartment.

FIG. 2 is a perspective view of a vehicle containing two adult occupantson the front seat with the vehicle shown in phantom illustrating onepreferred location of the ultrasonic transducers placed according to themethods taught in this invention.

FIG. 3 is a view as in FIG. 2 with the passenger occupant replaced by achild in a forward facing child seat.

FIG. 4 is a view as in FIG. 2 with the passenger occupant replaced by achild in a rearward facing child seat.

FIG. 5 is a view as in FIG. 2 with the passenger occupant replaced by aninfant in an infant seat.

FIG. 6 is a diagram illustrating the interaction of two ultrasonicsensors and how this interaction is used to locate a circle is space.

FIG. 7 is a view as in FIG. 2 with the occupants removed illustratingthe location of two circles in space and how they intersect the volumescharacteristic of a rear facing child seat and a larger occupant.

FIG. 8 illustrates a preferred mounting location of a three-transducersystem.

FIG. 9 illustrates a preferred mounting location of a four-transducersystem.

FIG. 10 is a plot showing the target volume discrimination for twotransducers.

FIG. 11 illustrates a preferred mounting location of a eight-transducersystem.

FIG. 12 is a chart of four typical raw signals which are combined toconstitute a vector.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

System Adaptation involves the process by which the hardwareconfiguration and the software algorithms are determined for aparticular vehicle. Each vehicle model or platform will most likely havea different hardware configuration and different algorithms. Some of thevarious aspects that make up this process are as follows:

The determination of the mounting location and aiming of thetransducers.

The determination of the transducer field angles.

The use of a neural network algorithm generating program such ascommercially available from NeuralWare to help generate the algorithms.

The process of the collection of data in the vehicle for neural networktraining purposes.

The method of automatic movement of the vehicle seats etc. while data iscollected.

The determination of the quantity of data to acquire and the setupsneeded to achieve a high system accuracy, typically several hundredthousand vectors.

The collection of data in the presence of varying environmentalconditions such as with thermal gradients.

The photographing of each data setup.

The makeup of the different databases and the use of three differentdatabases.

The method by which the data is biased to give higher probabilities forforward facing humans.

The automatic recording of the vehicle setup including seat, seat back,headrest, window, visor, armrest positions to help insure dataintegrity.

The use of a daily setup to validate that the transducer configurationhas not changed.

The method by which bad data is culled from the database.

The inclusion of the Fourier transforms and other pre-processors of thedata in the training process.

The use of multiple network levels, for example, for categorization andposition.

The use of multiple networks in parallel.

The use of post processing filters and the particularities of thesefilters.

The addition of fuzzy logic or other human intelligence based rules.

The method by which vector errors are corrected using, for example, aneural network.

The use of neural works as the pattern recognition algorithm generatingsystem.

The use of back propagation neural networks from training.

The use of vector normalization.

The use of feature extraction techniques including:

The number of data points prior to a peak.

The normalization factor.

The total number of peaks.

The vector mean or variance.

The use of other computational intelligence systems such as the geneticalgorithms.

The use the data screening techniques.

The techniques used to develop a stable network including the conceptsof a old and a new network.

The time spent or the number of iterations spent in, and method of,arriving at a stable network.

The technique where a small amount of data is collected first such as 16sheets followed by a complete data collection sequence.

The process of adapting the system to the vehicle begins with a surveyof the vehicle model. Any existing sensors, such as seat positionsensors, seat back sensors, etc., are immediate candidates for inclusioninto the system. Input from the customer will determine what types ofsensors would be acceptable for the final system. These sensors caninclude: seat structure mounted weight sensors, pad type weight sensors,pressure type weight sensors, seat fore and aft position sensors, seatvertical position sensors, seat angular position sensors, seat backposition sensors, headrest position sensors, ultrasonic occupantsensors, optical occupant sensors, capacitive sensors, inductivesensors, radar sensors, vehicle velocity and acceleration sensors, brakepressure, seatbelt force, payout and buckle sensors. etc. A candidatearray of sensors is then chosen and mounted onto the vehicle.

The vehicle is also instrumented so that data input by humans isminimized. Thus, the positions of the various components in the vehiclesuch as the seats, windows, sun visor, armrest, etc. are automaticallyrecorded. Also, the position of the occupant while data is being takenis also absolutely recorded through a variety of techniques such asdirect ultrasonic ranging sensors, optical ranging sensors, radarranging sensors, optical tracking sensors etc. Cameras are alsoinstalled to take a picture of the setup to correspond to each vector ofdata collected or at some other appropriate frequency.

A standard set of vehicle setups is chosen for initial trial datacollection purposes. Typically, the initial trial will consist ofbetween 20,000 and 100,000 setups.

Initial digital data collection now proceeds for the trial setup matrix.The data is collected from the transducers, digitized and combined toform to a vector of input data for analysis by a neural network program.This analysis should yield a training accuracy of nearly 100%. If thisis not achieved, then additional sensors are added to the system or theconfiguration changed and the data collection and analysis repeated.

In addition to a variety of seating states for objects in the passengercompartment, the trial database will also include environmental effectssuch as thermal gradients caused by heat lamps and the operation of theair conditioner and heater. A sample of such a matrix is presented inAppendix 1. After the neural network has been trained on the trialdatabase, the trial database will be scanned for vectors that yielderroneous results (which would likely be considered bad data). A studyof those vectors along with vectors from associated in time cases arecompared with the photographs to determine whether there is erroneousdata present. If so, an attempt is made to determine the cause of theerroneous data. If the cause can be found, for example if a voltagespike on the power line corrupted the data, then the vector will beremoved from the database and an attempt is made to correct the datacollection process so as to remove such disturbances.

At this time, some of the sensors may be eliminated from the sensormatrix. This can be determined during the neural network analysis byselectively eliminating sensor data from the analysis to see what theeffect if any results. Caution should be exercised here, however, sinceonce the sensors have been initially installed in the vehicle, itrequires little additional expense to use all of the installed sensorsin future data collection and analysis.

The neural network that has been developed in this first phase is usedduring the data collection in the next phases as a instantaneous checkon the integrity of the new vectors being collected. Occasionally, avoltage spike or other environmental disturbance will momentarily effectthe data from some transducers. It is important to capture this event tofirst eliminate that data from the database and second to isolate thecause of the erroneous data.

The next set of data to be collected is the training database. This willbe the largest database initially collected and will cover such setupsas listed, for example, in Appendix 1. The training database, which maycontain 500,000 or more vectors, will be used to begin training of theneural network. While this is taking place additional data will becollected according to Appendix 1 of the independent and validationdatabases. The training database has been selected so that it uniformlycovers all seated states that are known to be likely to occur in thevehicle. The independent database may be similar in makeup to thetraining database or it may evolve to more closely conform to theoccupancy state distribution of the validation database. During theneural network training, the independent database is used to check theaccuracy of the neural network and to reject a candidate neural networkdesign if its accuracy, measured against the independent database, isless than that of a previous network architecture.

Although the independent database is not actually used in the trainingof the neural network, nevertheless, it has been found that itsignificantly influences the network structure. Therefore, a thirddatabase, the validation or real world database, is used as a finalaccuracy check of chosen system. It is the accuracy against thisvalidation database that is considered to be the system accuracy. Thevalidation database is composed of vectors taken from setups whichclosely correlate with vehicle occupancy in real cars on the roadway.Initially the training database is the largest of the three databases.As time and resources permit the independent database, which perhapsstarts out with 100,000 vectors, will continue to grow until it becomesapproximately the same size as the training database. The validationdatabase, on the other hand, will typically start out with as few as50,000 vectors. However, as the hardware configuration is frozen, thevalidation database will continuously grow until, in some cases, itactually becomes larger than the training database. This is because nearthe end of the program, vehicles will be operating on highways and datawill be collected in real world situations. If in the real world tests,system failures are discovered this can lead to additional data beingtaken for both the training and independent databases as well as thevalidation database.

Once a network has been trained using all of the available data from allof the transducers, it is expected that the accuracy of the network willbe very close to 100%. It is usually not practical to use all of thetransducers that have been used in the training of the system for finalinstallation in real production vehicle models. This is primarily due tocost and complexity considerations. Usually the automobile manufacturerwill have an idea of how many sensors would be acceptable forinstallation in a production vehicle. For example, the data may havebeen collected using 20 different transducers but the automobilemanufacturer may restrict the final selection to 6 transducers. The nextprocess, therefore, is to gradually eliminate sensors to determine whatis the best combination of six sensors, for example, to achieve thehighest system accuracy. Ideally, a series of networks would be trainedusing all combinations of six sensors from the 20 available. Theactivity would require a prohibitively long time. Certain constraintscan be factored into the system from the beginning to start the pruningprocess. For example, it would probably not make sense to have bothoptical and ultrasonic sensors present in the same system since it wouldcomplicate the electronics. In fact, the automobile manufacturer mayhave decided initially that an optical system would be too expensive andtherefore would not be considered. The inclusion of optical sensors,therefore, serves as a way of determining the loss in accuracy as afunction of cost. Various constraints, therefore, usually allow theimmediate elimination of a significant number of the initial group ofsensors. This elimination and the training on the remaining sensorsprovides the resulting accuracy loss that results.

The next step is to remove each of the sensors one at a time anddetermine which sensor has the least effect on the system accuracy. Thisprocess is then repeated until the total number of sensors has beenpruned down to the number desired by the customer. At this point, theprocess is reversed to add in one at a time those sensors that wereremoved at previous stages. It has been found, for example, that asensor that appears to be unimportant during the early pruning processcan become very important later on. Such a sensor may add a small amountof information due to the presence of various other sensors. Whereas thevarious other sensors, however, may yield less information than stillother sensors and, therefore may have been removed during the pruningprocess. Reintroducing the sensor that was eliminated early in the cycletherefore can have a significant effect and can change the final choiceof sensors to make up the system.

The above method of reducing the number of sensors that make up thesystem is but one of a variety approaches which have applicability indifferent situations. In some cases a Monte Carlo or other statisticalapproach is warranted, whereas in other cases a design of experimentsapproach has proven to be the most successful. In many cases, anoperator conducting this activity becomes skilled and after a whileknows intuitively what set of sensors is most likely to yield the bestresults. During the process it is not uncommon to run multiple cases ondifferent computers simultaneously. Also, during this process, adatabase of the cost of accuracy is generated. The automobilemanufacturer, for example, may desire to have the total of 6 transducersin the final system, however, when shown the fact that the addition ofone or two additional sensors substantially increases the accuracy ofthe system, the manufacturer may change his mind. Similarly, the initialnumber of sensors selected may be 6 but the analysis could show that 4sensors give substantially the same accuracy as 6 and therefore theother 2 can be eliminated at a cost saving.

While the pruning process is occurring, the vehicle is subjected to avariety of road tests and would be subjected to presentations to thecustomer. The road tests are tests that are run at different locationsthan where the fundamental training took place. It has been found thatunexpected environmental factors can influence the performance of thesystem and therefore these tests can provide critical information. Thesystem, therefore, which is installed in the test vehicle should havethe capability of recording system failures. This recording includes theoutput of all of the sensors on the vehicle as well as a photograph ofthe vehicle setup that caused the error. This data is later analyzed todetermine whether the training, independent or validation setups need tobe modified and/or whether the sensors or positions of the sensorsrequire modification.

Once the final set of sensors has been chosen, the vehicle is againsubjected to real world testing on highways and at customerdemonstrations. Once again any failures are recorded. In this case,however, since the total number of sensors in the system is probablysubstantially less than the initial set of sensors, certain failures areto be expected. All such failures, if expected, are reviewed carefullywith the customer to be sure that the customer recognizes the systemfailure modes and is prepared to accept the system with those failuremodes.

The system described so far has been based on the use of a single neuralnetwork. It is frequently necessary to use multiple neural networks orother pattern recognition systems. For example, for determining theoccupancy state of a vehicle seat there are really two requirements. Thefirst requirement is to establish what is occupying the seat and thesecond requirement is to establish where that object is located.Generally, a great deal of time, typically many seconds, is availablefor determining whether a forward facing human or an occupied orunoccupied rear facing child seat, for example, occupies the vehicleseat. On the other hand, if the driver of the car is trying to avoid anaccident and is engaged in panic braking, the position of an unbeltedoccupant can be changing rapidly as he or she is moving toward theairbag. Thus, the problem of determining the location of an occupant istime critical. Typically, the position of the occupant in suchsituations must be determined in less than 20 milliseconds. There is noreason for the system to have to determine that a forward facing humanbeing is in the seat while simultaneously determining where that forwardfacing human being is. The system already knows that the forward facinghuman being is present and therefore all of the resources can be used todetermine the occupant's position. Thus, in this situation a dual levelneural network can be advantageously used. The first level determinesthe occupancy of the vehicle seat and the second level determines theposition of that occupant. In some rare situations, it has beendemonstrated that multiple neural networks used in parallel can providesome benefit. This will be discussed in more detail below.

The data that is fed to the pattern recognition system typically willusually not be the raw vectors of data as captured and digitized fromthe various transducers. Typically, a substantial amount ofpreprocessing of the data is undertaken to extract the importantinformation from the data that is fed to the neural network. This isespecially true in optical systems and where the quantity of dataobtained, if all were used by the neural network, would require veryexpensive processors. The techniques of preprocessing data will not bedescribed in detail here. However, the preprocessing techniquesinfluence the neural network structure in many ways. For example, thepreprocessing used to determine what is occupying a vehicle seat istypically quite different from the preprocessing used to determine thelocation of that occupant. Some particular preprocessing concepts willbe discussed in more detail below.

Once the pattern recognition system has been applied to the preprocesseddata, one or more decisions are available as output. The output from thepattern recognition system is usually based on a snapshot of the outputof the various transducers. Thus, it represents one epoch or timeperiod. The accuracy of such a decision can usually be substantiallyimproved if previous decisions from the pattern recognition system arealso considered. In the simplest form, which is typically used for theoccupancy identification stage, the results of many decisions areaveraged together and the resulting averaged decision is chosen as thecorrect decision. Once again, however, the situation is quite differentfor dynamic out-of-position. The position of the occupant must be knownat that particular epoch and cannot be averaged with his previousposition. On the other hand, there is information in the previouspositions that can be used to improve the accuracy of the currentdecision. For example, if the new decision says that the occupant hasmoved six inches since the previous decision, and, from physics, it isknown that this could not possibly take place, than a better estimate ofthe current occupant position can be made by extrapolating from earlierpositions. Alternately, an occupancy position versus time curve can befitted using a variety of techniques such as the least squaresregression method, to the data from previous 10 epochs, for example.This same type of analysis could also be applied to the vector itselfrather than to the final decision thereby correcting the data prior toits being entered into the pattern recognition system.

A pattern recognition system, such as a neural network, can sometimesmake totally irrational decisions. This typically happens when thepattern recognition system is presented with a data set or vector thatis unlike any vector that has been in its training set. The variety ofseating states of a vehicle is unlimited. Every attempt is made toselect from that unlimited universe a set of representative cases.Nevertheless, there will always be cases that are significantlydifferent from any that have been previously presented to the neuralnetwork. The final step, therefore, to adapting a system to a vehicle,is to add a measure of human intelligence. Sometimes this goes under theheading of fuzzy logic and the resulting system has been termed in somecases a neural fuzzy system. In some cases, this takes the form of anobserver studying failures of the system and coming up with rules andthat say, for example, that if sensor A perhaps in combination withanother sensor produces values in this range than the system should beprogrammed to override the pattern recognition decision and substitutetherefor a human decision.

An example of this appears in R. Scorcioni, K. Ng, M. M. Trivedi, N.Lassiter; “MoNiF: A Modular Neuro-Fuzzy Controller for Race CarNavigation”; In Proceedings of the 1997 IEEE Symposium on ComputationalIntelligence and Robotics Applications, Monterey, Calif., USA July 1997and describes the case of where an automobile was designed forautonomous operation and trained with a neural network, in one case, anda neural fuzzy system in another case. As long as both vehicles operatedon familiar roads both vehicles performed satisfactorily. However, whenplaced on an unfamiliar road, the neural network vehicle failed whilethe neural fuzzy vehicle continue to operate successfully. Naturally, ifthe neural network vehicle had been trained on the unfamiliar road, itmight very well have operated successful. Nevertheless, the criticalfailure mode of neural networks that most concerns people is thisuncertainty as to what a neural network will do when confronted with anunknown state.

One aspect, therefore, of adding human intelligence to the system, is toferret out those situations where the system is likely to fail.Unfortunately, in the current state-of-the-art, this is largely a trialand error activity. One example is that if the range of certain parts ofvector falls outside of the range experienced during training, thesystem defaults to a particular state. In the case of suppressingdeployment of one or more airbags, or other occupant protectionapparatus, this case would be to enable airbag deployment even if thepattern recognition system calls for its being disabled.

The foregoing description is applicable to the systems described in thefollowing drawings and the connection between the foregoing descriptionand the systems described below will be explained below. However, itshould be appreciated that the systems shown in the drawings do notlimit the applicability of the methods described above.

Referring to the accompanying drawings wherein like reference numbersdesignate the same or similar elements, FIG. 1 shows a passenger seat 1to which an adjustment apparatus including a seated-state detectingsystem developed according to the present invention may be applied. Theseat 1 includes a horizontally situated bottom seat portion 2 and avertically oriented back portion 3. The seat portion 2 is provided withweight measuring means, i.e., one or more weight sensors 6 and 7, thatdetermine the weight of the object occupying the seat, if any. Thecoupled portion between the seated portion 2 and the back portion 3(also referred to as the seatback) is provided with a reclining angledetecting sensor 9, which detects the tilted angle of the back portion 3relative to the seat portion 2. The seat portion 2 is provided with aseat track position-detecting sensor 10. The seat track positiondetecting sensor 10 fulfills a role of detecting the quantity ofmovement of the seat 1 which is moved from a back reference position,indicated by the dotted chain line. Embedded within the seatback 3 is aheartbeat sensor 31 and a motion sensor 33. Attached to the headliner ofthe vehicle is a capacitance sensor 32. The seat 1 may be the driverseat, the front passenger seat or any other seat in a motor vehicle aswell as other seats in transportation vehicles or seats innon-transportation applications.

The weight measuring means, such as the sensors 6 and 7, are associatedwith the seat, and can be mounted into or below the seat portion 2 or onthe seat structure, for example, for measuring the weight applied ontothe seat. The weight may be zero if no occupying item is present.Sensors 6 and 7 may represent a plurality of different sensors whichmeasure the weight applied onto the seat at different portions thereofor for redundancy purposes, for example, such as by means of an airbagor bladder 5 in the seat portion 2. The bladder 5 may have one or morecompartments. Such sensors may be in the form of strain, force orpressure sensors which measure the force or pressure on the seat portion2 or seat back 3, displacement measuring sensors which measure thedisplacement of the seat surface or the entire seat 1 such as throughthe use of strain gages mounted on the seat structural members, such as7, or other appropriate locations, or systems which convert displacementinto a pressure wherein a pressure sensor can be used as a measure ofweight.

An ultrasonic or optical sensor system 12 is mounted on the upperportion of the front pillar, A-Pillar, of the vehicle and a similarsensor system 11 is mounted on the upper portion of the intermediatepillar, B-Pillar. The outputs of the transducers 11 and 12 are input toa band pass filter 20 through a multiplex circuit 19 which is switchedin synchronization with a timing signal from the ultrasonic sensor drivecircuit 18, and then is amplified by an amplifier 21. The band passfilter 20 removes a low frequency wave component from the output signaland also removes some of the noise. The envelope wave signal is input toan analog/digital converter (ADC) 22 and digitized as measured data. Themeasured data is input to a processing circuit 23, which is controlledby the timing signal which is in turn output from the sensor drivecircuit 18

Each of the measured data is input to a normalization circuit 24 andnormalized. The normalized measured data is input to the neural networkcircuit 25 as wave data.

The output of the weight sensor(s) 6 and 7 is amplified by an amplifier26 coupled to the weight sensor(s) 6 and 7 and the amplified output isinput to the analog/digital converter 27.

The reclining angle detecting sensor 9 and the seat trackposition-detecting sensor 10 are connected to appropriate electroniccircuits. For example, a constant-current can be supplied from aconstant-current circuit to the reclining angle detecting sensor 9, andthe reclining angle detecting sensor 9 converts a change in theresistance value on the tilt of the back portion 3 to a specificvoltage. This output voltage is input to an analog/digital converter 28as angle data, i.e., representative of the angle between the backportion 3 and the seat portion 2. Similarly, a constant current can besupplied from a constant-current circuit to the seat track positiondetecting sensor 10 and the seat track position detecting sensor 10converts a change in the resistance value based on the track position ofthe seat portion 2 to a specific voltage. This output voltage is inputto an analog/digital converter 29 as seat track data. Thus, the outputsof the reclining angle-detecting sensor 9 and the seat track positioningsensor 10 are input to the analog/digital converters (ADC) 28 and 29,respectively Each digital data value from the ADCs 28,29 is input to theneural network circuit 25. A more detailed description of this andsimilar systems can be found in co-pending patent application Ser. No.09/128,490, which is incorporated herein by reference. The systemdescribed above is one example of many systems that can be designedusing the teachings of this invention for detecting the occupancy stateof the seat of a vehicle.

A section of the passenger compartment of an automobile is showngenerally as 100 in FIG. 2. A driver 101 of a vehicle sits on a seat 102behind a steering wheel, not shown, and an adult passenger 103 sits onseat 104 on the passenger side. Two transmitter and receiver assemblies110 and 111, also referred to herein as transducers, are positioned inthe passenger compartment 100, one transducer 110 is arranged on theheadliner adjacent or in proximity to the dome light and the othertransducer 111 is arranged on the center of the top of the dashboard.The methodology leading to the placement of these transducers is centralto the instant invention as explained in detail below. In thissituation, the system developed in accordance with this invention willreliably detect that an occupant is sitting on seat 104 and deploymentof the airbag is enabled in the event that the vehicle experiences acrash. Transducers 110, 111 are placed with their separation axisparallel to the separation axis of the head, shoulder and rear facingchild seat volumes of occupants of an automotive passenger seat and inview of this specific positioning, are capable of distinguishing thedifferent configurations. In addition to the ultrasonic transducers 110,111, weight-measuring sensors 210, 211, 212, 214 and 215 are alsopresent. These weight sensors may be of a variety of technologiesincluding, as illustrated here, strain-measuring transducers attached tothe vehicle seat support structure as described in more detail inco-pending patent application. Naturally other weight systems can beutilized including systems that measure the deflection of, or pressureon, the seat cushion. The weight sensors described here are meant to beillustrative of the general class of weight sensors and not anexhaustive list of methods of measuring occupant weight.

In FIG. 3, a forward facing child seat 120 containing a child 121replaces the adult passenger 103 as shown in FIG. 2. In this case, it isusually required that the airbag not be disabled in the event of anaccident. However, in the event that the same child seat is placed inthe rearward facing position as shown in FIG. 4, then the airbag isusually required to be disabled since deployment of the airbag in acrash can seriously injure or even kill the child. Furthermore, asillustrated in FIG. 5, if an infant 131 in an infant carrier 130 ispositioned in the rear facing position of the passenger seat, the airbagshould be disabled for the reasons discussed above. Instead of disablingdeployment of the airbag, the deployment could be controlled to provideprotection for the child, e.g., to reduce the force of the deployment ofthe airbag. It should be noted that the disabling or enabling of thepassenger airbag relative to the item on the passenger seat may betailored to the specific application. For example, in some embodiments,with certain forward facing child seats, it may in fact be desirable todisable the airbag and in other cases to deploy a depowered airbag. Theselection of when to disable, depower or enable the airbag, as afunction of the item in the passenger seat and its location, is madeduring the programming or training stage of the sensor system and, inmost cases, the criteria set forth above will be applicable, i.e.,enabling airbag deployment for a forward facing child seat and an adultin a proper seating position and disabling airbag deployment for arearward facing child seat and infant and for any occupant who isout-of-position and in close proximity to the airbag module. The sensorsystem developed in accordance with the invention may however beprogrammed according to other criteria.

Several systems using other technologies have been devised todiscriminate between the four cases illustrated above but none haveshown a satisfactory accuracy or reliability of discrimination. Some ofthese systems appear to work as long as the child seat is properlyplaced on the seat and belted in. So called “tag systems”, for example,whereby a device is placed on the child seat which iselectromagnetically sensed by sensors placed within the seat have notproven reliable by themselves but can add information to the overallsystem. When used alone, they function well as long as the child seat isrestrained by a seatbelt, but when this is not the case they have a highfailure rate. Since the seatbelt usage of the population of the UnitedStates is only about 50% at the present time, it is quite likely that asignificant percentage of child seats will not be properly belted ontothe seat and thus children will be subjected to injury and death in theevent of an accident.

The methodology of this invention was devised to solve this problem. Tounderstand this methodology, consider two ultrasonic transmitters andreceivers 110 and 111 (transducers) which are connected by an axis AB inFIG. 6. Each transmitter radiates a signal which is primarily confinedto a cone angle, called the field angle, with its origin at thetransmitter. For simplicity, assume that the transmitter and receiverare the same device although in some cases a separate device will beused for each function. When a transducer sends out a burst of waves, tothereby irradiate the passenger compartment with ultrasonic radiation,and then receives a reflection or modified radiation from some object inthe passenger compartment, the distance of the object from thetransducer can be determined by the time delay between the transmissionof the waves and the reception of the reflected or modified waves.

When looking at a single transducer, it is not possible to determine thedirection to the object which is reflecting or modifying the signal butit is possible to know only how far that object is from the transducer,that is a single transducer enables a distance measurement but not adirectional measurement. In other words, the object may be at a point onthe surface of a three-dimensional spherical segment having its originat the transducer and a radius equal to the distance. Consider twotransducers, such as 110 and 111 in FIG. 6, and both transducers receivea reflection from the same object, which is facilitated by properplacement of the transducers, the timing of the reflections depends onthe distance from the object to each respective transducer. If it isassumed for the purposes of this analysis that the two transducers actindependently, that is, they only listen to the reflections of waveswhich they themselves transmitted, then each transducer knows thedistance to the reflecting object but not its direction. If we assumethat the transducer radiates ultrasound in all directions within thefield cone angle, each transducer knows that the object is located on aspherical surface A′, B′ a respective known distance from thetransducer, that is, each transducer knows that the object is a specificdistance from that transducer which may or may not be the same distancebetween the other transducer and the same object. Since now there aretwo transducers, and the distance of the reflecting object is knownrelative to each of the transducers, the actual location of the objectresides on a circle which is the intersection of the two sphericalsurfaces A′, and B′. This circle is labeled C in FIG. 6. At each pointalong circle C, the distance to the transducer 110 is the same and thedistance to the transducer 111 is the same. This, of course, is strictlytrue only for ideal one-dimensional objects.

For many cases, the mere knowledge that the object lies on a particularcircle is sufficient since it is possible to locate the circle such thatthe only time that an object lies on a particular circle that itslocation is known. That is, the circle which passes through the area ofinterest otherwise passes through a volume where no objects can occur.Thus, the mere calculation of the circle in this specific location,which indicates the presence of the object along that circle, providesvaluable information concerning the object in the passenger compartmentwhich may be used to control or affect another system in the vehiclesuch as the airbag system. This of course is based on the assumptionthat the reflections to the two transducers are in fact from the sameobject. Care must be taken in locating the transducers such that otherobjects do not cause reflections that could confuse the system.

FIG. 7 for example illustrates two circles D and E, of interest whichrepresent the volume which is usually occupied when the seat is occupiedby a person not in a child seat, C, or by a forward facing child seatand the volume normally occupied by a rear facing child seat,respectively. Thus, if the circle generated by the system, (i.e., byappropriate processor means which receives the distance determinationfrom each transducer and creates the circle from the intersection of thespherical surfaces which represent the distance from the transducers tothe object) is at a location which is only occupied by an adultpassenger, the airbag would not be disabled since its deployment in acrash is desired. On the other hand, if a circle is at a locationoccupied only by a rear facing child seat, the airbag would be disabled.

The above discussion of course is simplistic in that it is not take intoaccount the volume occupied by the object or the fact the reflectionsfrom more than one object surface will be involved. In reality,transducer B is likely to pickup the rear of the occupant's head andtransducer A, the front. This makes the situation more difficult for anengineer looking at the data to analyze. It has been found that patternrecognition technologies are able to extract the information from thesesituations and through a proper application of these technologies, analgorithm can be developed, which when installed as part of the systemfor a particular vehicle, the system accurately and reliablydifferentiates between a forward facing and rear facing child seat, forexample, or an in-position or out-of-position forward facing humanbeing.

From the above discussion, a method of transducer location is disclosedwhich provides unique information to differentiate between (i) a forwardfacing child seat or a forward properly positioned occupant where airbagdeployment is desired and (ii) a rearward facing child seat and anout-of-position occupant where airbag deployment is not desired. Inactuality, the algorithm used to implement this theory does not directlycalculate the surface of spheres or the circles of interaction ofspheres. Instead, a pattern recognition system is used to differentiateairbag-deployment desired cases from those where the airbag should notbe deployed. For the pattern recognition system to accurately performits function, however, the patterns presented to the system must havethe requisite information. That is, a pattern of reflected waves from anoccupying item in a passenger compartment to various transducers must beuniquely different for cases where airbag deployment is desired fromcases where deployment is not desired. The theory described above and inmore detail below teaches how to locate transducers within the vehiclepassenger compartment so that the patterns of reflected waves will beeasily distinguishable for cases where airbag deployment is desired fromthose where deployment is not desired. In the case presented thus far,it has been shown that in some implementations the use of only twotransducers can result in the desired pattern differentiation when thevehicle geometry is such that two transducers can be placed such thatthe circles D (airbag enabled) and E (airbag disabled) fall outside ofthe transducer field cones except where they are in the critical regionswhere positive identification of the condition occurs. Thus, the aimingand field angle of the transducers are important factors to determine inadapting a system to a particular vehicle.

The use of only two transducers in a system is typically not acceptablesince one or both of the transducers can be rendered inoperable by beingblocked, for example, by a newspaper. Thus, it is desirable to add athird transducer 112 as shown in FIG. 8 which now provides a third setof spherical surfaces relative to the third transducer. Transducer 112is positioned on the passenger side of the A-pillar (which is apreferred placement if the system is designed to operate on thepassenger side of the vehicle). Three spherical surfaces now intersectin only two points and in fact, usually at one point if the aimingangles and field angles are properly chosen. Once again, this discussionis only strictly true for a point object. For a real object, thereflections will come from different surfaces of the object, whichusually are at similar distances from the object. Thus, the addition ofa third transducer substantially improves system reliability. Finally,with the addition of a fourth transducer 113 as shown in FIG. 9, evengreater accuracy and reliability is attained. Transducer 113 ispositioned on the ceiling of the vehicle close to the passenger sidedoor. In FIG. 9, lines connecting the transducers C and D and thetransducers A and B are substantially parallel permitting an accuratedetermination of asymmetry and thereby object rotation. Thus, forexample, if the infant seat is placed on an angle as shown in FIG. 5,this condition can be determined and taken into account when thedecision is made to disable the deployment of the airbag.

The discussion above has centered on locating transducers and designinga system for determining whether the two target volumes, that adjacentthe airbag and that adjacent the upper portion of the vehicle seat, areoccupied. Other systems have been described in the above referencedpatents using a sensor mounted on or adjacent the airbag module and asensor mounted high in the vehicle to monitor the space near the vehicleseat. Such systems use the sensors as independent devices and do not usethe combination of the two sensors to determine where the object islocated. In fact, the location of such sensors is usually poorly chosenso that it is easy to blind either or both with a newspaper, forexample. Furthermore, no system is known to have been disclosed, exceptin patents and patent applications assigned to the assignee of thisinvention, which uses more than two transducers in such a manner thatone or more can be blocked without causing serious deterioration of thesystem. Again, the examples here have been for the purpose ofsuppressing the deployment of the airbag when it is necessary to preventinjury. The sensor system disclosed can be used for many other purposessuch as disclosed in the above-mentioned patent applications assigned tothe same assignee as the instant invention. The ability to use thesensors for these other applications in generally lacking in the systemsdisclosed in the other referenced patents.

Considering once again the condition of FIGS. 2-7 where two transducersare used, a plot can be made showing the reflection times of the objectswhich are located in the region of curve E and curve F of FIG. 7. Thisplot is shown on FIG. 10 where the c's represent reflections from rearfacing child seats from various tests where the seats were placed in avariety of different positions and similarly the s's and h's representshoulders and heads respectively of various forward facing humanoccupants. In these results from actual experiments, the effect of bodythickness is present and yet the results still show that the basicprinciples of separation of key volumes are valid. Note that there is aregion of separation between corridors that house the different objectclasses. It is this fact which is used in conjunction with neuralnetworks, as described in the above referenced patent applications,which permit the design of a system that provides an accuratediscrimination of rear facing child seats from forward facing humans.Heretofore before the techniques for locating the transducers toseparate these two zones were discovered, the entire discrimination taskwas accomplished using neural networks. There was significant overlapbetween the reflections from the various objects and thereforeseparation was done based on patterns of the reflected waves. By usingthe technology described herein to carefully orient the transducers soas to create this region of separation of the critical surfaces, whereinall of the rear facing child seat data falls within a known corridor,the task remaining for the neural networks is substantially simplifiedwith the result that the accuracy of identification is substantiallyimproved.

Three general classes of child seats exist as well as several modelswhich are unique. First, there is the infant only seat as shown in FIG.5 which is for occupants weighing up to about 20 pounds. This isdesigned to be only placed in the rear facing position. The second whichis illustrated in FIGS. 2 and 3 is for children from about 20 to about40 pounds and can be used in both the forward and rear facing positionand the third is for use only in the forward facing position and is forchildren weighing over about 40 pounds. All of these seats as well asthe unique models are used in test setups according to this inventionfor adapting a system to a vehicle. For each child seat, there areseveral hundred unique orientations representing virtually everypossible position of that seat within the vehicle. Tests are run, forexample, with the seat tilted 22 degrees, rotated 17 degrees, placed onthe front of the seat with the seat back fully up with the seat fullyback and with the window open as well as all variations of thereparameters. A large number of cases are also run, when practicing theteachings of this invention, with various accessories, such as clothing,toys, bottles, blankets etc., added to the child seat.

Similarly, wide variations are used for the occupants including size,clothing and activities such as reading maps or newspapers, leaningforward to adjust the radio, for example. Also included are cases wherethe occupant puts his/her feet on the dashboard or otherwise assumes awide variety of unusual positions. When all of the above configurationsare considered along with many others not mentioned, the total number ofconfigurations which are used to train the pattern recognition systemcan exceed 500,000. The goal is to include in the configuration trainingset representations of all occupancy states that occur in actual use.Since the system is highly accurate in making the correct decision forcases which are similar to those in the training set, the total systemaccuracy increases as the size of the training set increases providingthe cases are all distinct and not copies of other cases.

In addition to all of the variations in occupancy states, it isimportant to consider environmental effects during the data collection.Thermal gradients or thermal instabilities are particularly importantsince sound waves can be significantly diffracted by density changes inair. There are two aspects of the use of thermal gradients orinstability in training. First, the fact that thermal instabilitiesexist and therefore data with thermal instabilities present should bepart of database. For this case, a rather small amount of data collectedwith thermal instabilities would be used. A much more important use ofthermal instability comes from the fact that they add variability todata. Thus, considerably more data is taken with thermal instability andin fact, in some cases almost the entire database is taken with timevarying thermal gradients in order to provide variability to the data sothat the neural network does not memorize but instead generalizes fromthe data. This is accomplished by taking the data with a cold vehiclewith the heater operating and with a hot vehicle with the airconditioner operating. Additional data is also taken with a heat lamp ina closed vehicle to simulate a stable thermal gradient caused by sunloading.

To collect data for 500,000 vehicle configurations is not a formidabletask. A trained technician crew can typically collect data on in excesson 2000 configurations or vectors per hour. The data is collectedtypically every 50 to 100 milliseconds. During this time, the occupantis continuously moving, assuming a continuously varying position andposture in the vehicle including moving from side to side, forward andback, twisting his/her head, reading newspapers and books, moving hands,arms, feet and legs, until the desired number of different seated stateexamples are obtained. In some cases, this process is practiced byconfining the motion of an occupant into a particular zone. In somecases, for example, the occupant is trained to exercise these differentseated state motions while remaining in a particular zone that may bethe safe zone, the keep out zone, or an intermediate gray zone. In thismanner, data is collected representing the airbag disable, depoweredairbag enabled or fill power airbag enabled states. In other cases, theactual position of the back of the head and/or the shoulders of theoccupant are tracked using string pots, high frequency ultrasonictransducers, or optically. In this manner, the position of the occupantcan be measured and the decision as to whether this should be a disableor enable airbag case can be decided later. By continuously monitoringthe occupant, an added advantage results in that the data can becollected to permit a comparison of the occupant from one seated stateto another. This is particularly valuable in attempting to project thefuture location of an occupant based on a series of past locations aswould be desirable for example to predict when an occupant would crossinto the keep out zone during a panic braking situation prior to crash.

It is important to note that it is not necessary to train on everyvehicle produced but rather to train on each platform. A platform is anautomobile manufacturer's designation of a group of vehicle models thatare built on the same vehicle structure.

A review of the literature on neural networks yields the conclusion thatthe use of such a large training set is unique in the neural networkfield. The rule of neural networks is that there must be at least threetraining cases for each network weight. Thus, for example, if a neuralnetwork has 156 input nodes, 10 first hidden layer nodes, 5 secondhidden layer nodes, and one output node this results in a total of 1,622weights. According to conventional theory 5000 training examples shouldbe sufficient. It is highly unexpected, therefore, that greater accuracywould be achieved through 100 times that many cases. It is thus notobvious and cannot be deduced from the neural network literature thatthe accuracy of the system will improve substantially as the size of thetraining database increases even to tens of thousands of cases. It isalso not obvious looking at the plots of the vectors obtained usingultrasonic transducers that increasing the number of tests or thedatabase size will have such a significant effect on the systemaccuracy. Each of the vectors is a rather course plot with a fewsignificant peaks and valleys. Since the spatial resolution of thesystem is typically about 3 to 4 inches, it is once again surprisingthat such a large database is required to achieve significant accuracyimprovements.

Process for Training a Vehicle

The process for adapting an ultrasonic system to a vehicle will now bedescribed. A more detailed list of steps is provided in Appendix 3.Although the pure ultrasonic system is described here, a similar set ofsteps applies when other technologies such as weight and optical systemsare used. This description is thus provided to be exemplary and notlimiting:

1. Select transducer and horn designs to fit the vehicle. At this stage,usually full horns are used which are mounted so that they project intothe passenger compartment. No attempt is made at this time to achieve anesthetic matching of the transducers to the vehicle surfaces. Anestimate of the desired transducer fields are made at this time eitherfrom measurements in the vehicle directly or from CAD drawings.

2. Make polar plots of the transducer sonic fields. Transducers andcandidate horns are assembled and tested to confirm that the desiredfield angles have been achieved. This frequently requires someadjustment of the transducers in the horn.

3. Check to see that the fields cover the required volumes of thevehicle passenger compartment and do not impinge on adjacent flatsurfaces that may cause multipath effects. Redesign horns if necessary.

4. Install transducers into vehicle.

5. Map transducer fields in the vehicle and check for multipath effectsand proper coverage.

6. Adjust transducer aim and re-map fields if necessary.

7. Install daily calibration fixture and take standard setup data.

8. Acquire 50,000 to 100,000 vectors.

9. Adjust vectors for volume considerations by removing some initialdata points if cross talk is present and some final points to keep datain the desired passenger compartment volume.

10. Normalize vectors.

11. Run neural network algorithm generating software to create algorithmfor vehicle installation.

12. Check the accuracy of the algorithm. If not sufficiently accuratecollect more data where necessary and retrain. If still not sufficientlyaccurate, add additional transducers to cover holes.

13. When sufficient accuracy is attained, proceed to collect ˜500,000training vectors varying:

Occupancy (see Appendices 1 and 3):

Occupant size, position (zones), clothing etc

Child seat type, size, position etc.

Empty seat

Vehicle configuration:

Seat position

Window position

Visor and armrest position

Presence of other occupants in adjoining seat or rear seat

Temperature

Temperature gradient—stable

Temperature turbulence—heater and air conditioner

Wind turbulence—High speed travel with windows open, top down etc

14. Collect ˜100,000 vectors of Independent data using othercombinations of the above.

15. Collect ˜50,000 vectors of “real world data” to represent theacceptance criteria and more closely represent the actual seated stateprobabilities in the real world.

16. Train network and create algorithm using the training vectors andthe Independent data vectors.

17. Validate the algorithm using the real world vectors.

18. Install algorithm into the vehicle and test.

19. Decide on post processing methodology to remove final holes insystem.

20. Implement post-processing methods into the algorithm.

21. Final test. The process up until step 13 involve the use oftransducers with full horns mounted on the surfaces of the interiorpassenger compartment. At some point, the actual transducers which areto be used in the final vehicle must be substituted for the trialtransducers. This is either done prior to step 13 or at this step. Thisprocess involves designing transducer holders that blend with the visualsurfaces of the passenger compartment so that they can be covered with ascreen or mesh to retain the esthetic quality of the interior. This isusually a lengthy process and involves several consultations with thecustomer. Usually, therefore, the steps from 13 through 20 are repeatedat this point after the final transducer and holder design has beenselected. The initial data taken with full horns gives a measure of thebest system that can be made to operate in the vehicle. Some degradationin performance is expected when the esthetic horns are substituted forthe full horns. By conducting two complete data collection cycles anaccurate measure of this accuracy reduction can be obtained.

22. Ship to customers to be used in production vehicles.

23. Collect additional real world validation data for continuousimprovement.

More detail on the operation of the transducers and control circuitry aswell as the neural network is provided in the above referenced patentsand patent applications and is included herein as if the entire text ofthe same were reproduced here. One particular example of a successfulneural network for the two transducer case had 78 input nodes, 6 hiddennodes and one output node and for the four transducer case had 176 inputnodes 20 hidden layer nodes on hidden layer one, 7 hidden layer nodes onhidden layer 2 and one output node. The weights of the network weredetermined by supervised training using the back propagation method asdescribed in the referenced patent applications and in more detail inthe references cited therein. Naturally other neural networkarchitectures are possible including RCE, Logic on Projection,Stochastic etc.

Finally, the system is trained and tested with situations representativeof the manufacturing and installation tolerances that occur during theproduction and delivery of the vehicle as well as usage anddeterioration effects. Thus, for example, the system is tested with thetransducer mounting positions shifted by up to one inch in any directionand rotated by up to 15 degrees, with a simulated accumulation of dirtand other variations. This tolerance to vehicle variation also sometimespermits the installation of the system onto a different but similarmodel vehicle with, in many cases, only minimal retraining of thesystem.

The speed of sound varies with temperature, humidity, and pressure. Thiscan be compensated for by using the fact that the geometry between thetransducers is known and the speed of sound can therefore be measured.Thus, on vehicle startup and as often as desired thereafter, the speedof sound can be measured by one transducer, such as transducer 110 inFIG. 5, sending a signal which is directly received by anothertransducer. Since the distance separating them is known, the speed ofsound can be calculated and the system automatically adjusted to removethe variation due to the change in the speed of sound. Therefore, thesystem operates with same accuracy regardless of the temperature,humidity or atmospheric pressure. It may even be possible to use thistechnique to also automatically compensate for any effects due to windvelocity through an open window. An additional benefit of this system isthat it can be used to determine the vehicle interior temperature foruse by other control systems within the vehicle since the variation inthe velocity of sound is a strong function of temperature and a weakfunction of pressure and humidity.

The problem with the speed of sound measurement described above is thatsome object in the vehicle may block the path from one transducer toanother. This of course could be checked and a correction not be made ifthe signal from one transducer does not reach the other transducer. Theproblem, however, is that the path might not be completely blocked butonly slightly blocked. This would cause the ultrasonic path length toincrease, which would give a false indication of a temperature change.This can be solved by using more than one transducer. All of thetransducers can broadcast signals to all of the other transducers. Theproblem here, of course, is which transducer pair does one believe ifthey all give different answers. The answer is the one that gives theshortest distance or the greatest calculated speed of sound. By thismethod, there are a total of 6 separate paths for four ultrasonictransducers.

An alternative method of determining the temperature is to use thetransducer circuit to measure some parameter of the transducer thatchanges with temperature. For example the natural frequency ofultrasonic transducers changes in a known manner with temperature andtherefore by measuring the natural frequency of the transducer thetemperature can be determined. Since this method does not requirecommunication between transducers, it would also work in situationswhere each transducer has a different resonant frequency.

The process by which all of the distances are carefully measured fromeach transducer to the other transducers and the algorithm developed todetermine the speed of sound, is a significant part of the teachings ofthe instant invention. Prior to this, the speed of sound calculation wasbased on a single transmission from one transducer to a known secondtransducer. This resulted in an inaccurate system design and degradedthe accuracy of systems in the field.

Another important feature of a system, developed in accordance with theteachings of this invention, is the realization that motion of thevehicle can be used in a novel manner to substantially increase theaccuracy of the system. Ultrasonic waves reflect on most objects aslight off a mirror. This is due to the relatively long wavelength ofultrasound as compared with light. As a result, certain reflections canoverwhelm the receiver and reduce the available information. Whenreadings are taken while the occupant and/or the vehicle is in motion,and these readings averaged over several transmission/reception cycles,the motion of the occupant and vehicle causes various surfaces to changetheir angular orientation slightly but enough to change the reflectivepattern and reduce this mirror effect. The net effect is that theaverage of several cycles gives a much clearer image of the reflectingobject than is obtainable from a single cycle. This then provides abetter image to the neural network and significantly improves theidentification accuracy of the system. The choice of the number ofcycles to be averaged depends on the system requirements. For example,if dynamic out-of-position is required then each vector must be usedalone and averaging in the simple sense cannot be used. This will bediscussed more detail below.

When an occupant is sitting in the vehicle during normal vehicleoperation, the determination of the occupancy state can be substantiallyimproved by using successive observations over a period of time. Thiscan either be accomplished by averaging the data prior to insertion intoa neural network, or alternately the decision of the neural network canbe averaged. This is known as the categorization phase of the process.During categorization the occupancy state of the vehicle is determined.Is the vehicle occupied by the forward facing human, an empty seat, arear facing child seat, or an out-of-position human? Typically manyseconds of data can be accumulated to make the categorization decision.

When a driver senses an impending crash, on the other hand, he or shewill typically slam on the brakes to try to slow vehicle prior toimpact. If an occupant is unbelted, he or she will begin moving towardthe airbag during this panic braking. For the purposes of determiningthe position of the occupant, there is not sufficient time to averagedata as in the case of categorization. Nevertheless, there isinformation in data from previous vectors that can be used to partiallycorrect errors in current vectors, which may be caused by thermaleffects, for example. One method is to determine the location of theoccupant using the neural network based on previous training. The motionof the occupant can then be compared to a maximum likelihood positionbased on the position estimate of the occupant at previous vectors.Thus, for example, perhaps the existence of thermal gradients in thevehicle caused an error in the current vector leading to a calculationthat the occupant has moved 12 inches since the previous vector. Sincethis could be a physically impossible move during ten milliseconds, themeasured position of the occupant can be corrected based on his previouspositions and known velocity. Naturally, if an accelerometer is presentin the vehicle and if the acceleration data is available for thiscalculation, a much higher accuracy prediction can be made. Thus, thereis information in the data in previous vectors as well as in thepositions of the occupant determined from the this data that can be usedto correct erroneous data in the current vector and, therefore, in amanner not too dissimilar from the averaging method for categorization,the position accuracy of the occupant can be known with higher accuracy.

Returning to the placement of ultrasonic transducers for the ultrasonicoccupant position sensor system, as to the more novel features of theinvention for the placement of ultrasonic transducers, this applicationdiscloses (1) the application of two sensors to single-axis monitoringof target volumes; (2) the method of locating two sensors spanning atarget volume to sense object positions, that is, transducers aremounted along the sensing axis beyond the objects to be sensed, (3) themethod of orientation of the sensor axis for optimal targetdiscrimination parallel to the axis of separation of distinguishingtarget features; and (4) the method of defining the head and shouldersand supporting surfaces as defining humans for rear facing child seatdetection and forward facing human detection.

Considerable work is ongoing to improve the resolution of the ultrasonictransducers. To take advantage of higher resolution transducers, morecloser together data points should be obtained. This means that afterthe envelope has been extracted from the returned signals, the samplingrate should be increased from approximately 1000 samples per second toperhaps 2000 samples per second or even higher. By doubling or triplingthe amount data required to be analyzed, the system which is mounted onthe vehicle will require greater computational power. This results in amore expensive electronic system. Not all of the data is of equalimportance, however. The position of the occupant in the normal seatingposition does not need to be known with great accuracy whereas as thatoccupant is moving toward the keep out zone boundary during precrashbraking, the spatial accuracy requirements become more important.Fortunately, the neural network algorithm generating system has thecapability of indicating to the system designer the relative value ofeach of the data points used by the neural network. Thus, as many as,for example, 500 data points per vector may be collected and fed to theneural network during the training stage and, after careful pruning, thefinal number of data points to be used by the vehicle mounted system maybe reduced to 150, for example. This technique of using the neuralnetwork algorithm-generating program to prune the input data is animportant teaching of the present invention. By this method, theadvantages of higher resolution transducers can be optimally usedwithout increasing the cost of the electronic vehicle mounted circuits.Also, once the neural network has determined the spacing of the datapoints, this can be fine-tuned, for example, by acquiring more datapoints at the edge of the keep out zone as compared to positions wellinto the safe zone. The initial technique is done be collecting the full500 data points, for example, while in the system installed in thevehicle the data digitization spacing can be determined by hardware orsoftware so that only the required data is acquired.

The technique that was described above for the determination of thelocation of an occupant during panic or braking pre-crash situationsinvolved the use of a modular neural network. In that case, one neuralnetwork was used to determine the occupancy state of the vehicle and thesecond neural network was used to determine the location of the occupantwithin the vehicle. The method of designing a system utilizing multipleneural networks is a key teaching of the present invention. When thisidea is generalized, many potential combinations of multiple neuralnetwork architectures become possible. Some of these will now bediscussed.

One of the earliest attempts to use multiple neural networks was tocombine different networks trained differently but on substantially thesame data under the theory that the errors which affect the accuracy ofone network would be independent of the errors which affect the accuracyof another network. For example, for a system containing four ultrasonictransducers, four neural networks could be trained each using adifferent subset of the four transducer data. Thus, if the transducersare arbitrarily labeled A, B, C and D the then the first neural networkwould be trained on data from A, B and C. The second neural networkwould be trained on data from B, C, and D etc. This technique has notmet with a significant success since it is an attempt to mask errors inthe data rather than to eliminate them. Nevertheless, such a system doesperform marginally better in some situations compared to a singlenetwork using data from all four transducers. The penalty for using sucha system is that the computational time is increased by approximately afactor of three. This significantly affects the cost of the systeminstalled in a vehicle.

An alternate method of obtaining some of the advantages of the parallelneural network architecture described above, is to form a single neuralnetwork but where the nodes of one or more of the hidden layers are notall connected to all of the input nodes. Alternately, if the secondhidden layer is chosen, all of the notes from the previous hidden layerare not connected to all of the nodes of the subsequent layer. Thealternate groups of hidden layer nodes can then feed to different outputnotes and the results of the output nodes combined, either through aneural network training process into a single decision or a votingprocess. This latter approach retains most of the advantages of theparallel neural network while substantially reducing the computationalcomplexity.

The fundamental problem with parallel networks is that they focus onachieving reliability or accuracy by redundancy rather than by improvingthe neural network architecture itself or the quality of the data beingused. They also increase the cost of the final vehicle installedsystems. Alternately, modular neural networks improve the accuracy ofthe system by dividing up the tasks. For example, if a system is to bedesigned to determine the type of tree and the type of animal in aparticular scene, the modular approach would be to first determinewhether the object of interest is an animal or a tree and then useseparate neural networks to determine type of tree and the type ofanimal. When a human looks at a tree he is not ask himself is that atiger or a monkey. Modular neural network systems are efficient sinceonce the categorization decision is made, the seat is occupied byforward facing human, for example, the location of that object can bedetermined more accurately and without requiring increased computationalresources.

Another example where modular neural networks have proven valuable isprovide a means for separating “normal” from “special cases”. It hasbeen found that in some cases, the vast majority of the data falls intowhat might be termed “normal” cases that are easily identified with aneural network. The balance of the cases cause the neural networkconsiderable difficulty, however, there are identifiable characteristicsof the special cases that permits them to be separated from the normalcases and dealt with separately. Various types of human intelligencerules can be used, in addition to a neural network, to perform thisseparation including fuzzy logic, statistical filtering using theaverage class vector of normal cases, the vector standard deviation, andthreshold where a fuzzy logic network is used to determine chance of avector belonging to a certain class. If the chance is below a threshold,the standard neural network is used and if above the special one isused.

Mean-Variance connections, Fuzzy Logic, Stochastic, and GeneticAlgorithm networks, and combinations thereof such as Neuro-Fuzzy systemsare other technologies considered. During the process of designing asystem to be adapted to a particular vehicle, many different neuralnetwork architectures are considered including those mentioned above.The particular choice of architecture is frequently determined on atrial and error basis by the system designer. Although the parallelarchitecture system described above has not proven to be in generalbeneficial, one version of this architecture has shown some promise. Itis known that when training a neural network, that as the trainingprocess proceeds the accuracy of the decision process improves for thetraining and independent databases. It is also known that the ability ofthe network to generalize suffers. That is, when the network ispresented with a system which is similar to some case in the databasebut still with some significant differences, the network may make theproper decision in the early stages of training, but the wrong decisionsafter the network has become fully trained. This is sometimes called theyoung network vs. old network dilemma. In some cases, therefore, usingan old network in parallel with a young network can retain some of theadvantages of both networks, that is, the high accuracy of the oldnetwork coupled with the greater generality of the young network. Onceagain, the choice of any of these particular techniques is part of theprocess of designing a system to be adapted to a particular vehicle andis the prime subject of this invention. The particular combination oftools used depends on the particular application and the experience ofthe system designer.

The methods above have been described in connection with the use ofultrasonic transducers. Many of the methods, however, are alsoapplicable to optical, radar and other sensing systems and whereapplicable, this invention is not limited to ultrasonic systems. Inparticular, an important feature of this invention is the properplacement of three or more separately located receivers such that thesystem still operates with high reliability if one of the receivers isblocked by some object such as a newspaper. This feature is alsoapplicable to systems using electromagnetic radiation instead ofultrasonic, however the particular locations will differ based on theproperties of the particular transducers. Optical sensors based ontwo-dimensional cameras or other image sensors, for example, are moreappropriately placed on the sides of a rectangle surrounding the seat tobe monitored rather than at the comers of such a rectangle as is thecase with ultrasonic sensors. This is because ultrasonic sensors measurean axial distance from the sensor where the camera is most appropriatefor measuring distances up and down and across its field view ratherthan distances to the object. With the use of electromagnetic radiationand the advances which have recently been made in the field of very lowlight level sensitivity, it is now possible, in some implementations, toeliminate the transmitters and use background light as the source ofillumination along with using a technique such as auto-focusing toobtain the distance from the receiver to the object. Thus, onlyreceivers would be required further reducing the complexity of thesystem.

Although implicit in the above discussion, an important feature of thisinvention which should be emphasized is the method of developing asystem having distributed transducer mountings. Other systems which haveattempted to solve the rear facing child seat (RFCS) and out-of-positionproblems have relied on a single transducer mounting location or atmost, two transducer mounting locations. Such systems can be easilyblinded by a newspaper or by the hand of an occupant, for example, whichis imposed between the occupant and the transducers. This problem isalmost completely eliminated through the use of three or moretransducers which are mounted so that they have distinctly differentviews of the passenger compartment volume of interest. If the system isadapted using four transducers as illustrated in the distributed systemof FIG. 9, for example, the system suffers only a slight reduction inaccuracy even if two of the transducers. are covered so as to make theminoperable.

It is important in order to obtain the full advantages of the systemwhen a transducer is blocked, that the training and independentdatabases contains many examples of blocked transducers. If the patternrecognition system, the neural network in this case, has not beentrained on a substantial number of blocked transducer cases, it will notdo a good job in recognizing such cases later. This is yet anotherinstance where the makeup of the databases is crucial to the success ofdesigning the system that will perform with high reliability in avehicle and is an important aspect of the instant invention.

Other techniques which may or may not be part of the process ofdesigning a system for a particular application include the following:

1. Fuzzy logic. Neural networks frequently exhibit the property thatwhen presented with a situation that is totally different from anypreviously encounter, an irrational decision can result. Frequently whenthe trained observer looks at input data, certain boundaries to the databecome evident and cases that fall outside of those boundaries areindicative of either corrupted data or data from a totally unexpectedsituation. It is sometimes desirable for the system designer to addrules to handle these cases. These can be fuzzy logic based rules orrules based on human intelligence. One example would be that whencertain parts of the data vector fall outside of expected bounds thatthe system defaults to an airbag enable state.

2. Genetic algorithms. When developing a neural network algorithm for aparticular vehicle, there is no guarantee that the best of all possiblealgorithms has been selected. One method of improving the probabilitythat the best algorithm has been selected is to incorporate some of theprinciples of genetic algorithms. In one application of this theory, thenetwork architecture and/or the node weights are varied pseudo-randomlyto attempt to find other combinations which have higher success rates.The discussion of such genetic algorithms systems appears in the bookComputational Intelligence referenced above.

3. Pre-processing. For military target recognition is common to use theFourier transform of the data rather than the data itself. This can beespecially valuable for categorization as opposed to location of theoccupant and the vehicle. When used with a modular network, for example,the Fourier transform of the data may be used for the categorizationneural network and the non-transformed data used for the positiondetermination neural network. Recently wavelet transforms have also beenconsidered as a preprocessor.

4. Occupant position determination comparison. Above, under the subjectof dynamic out-of-position, it was discussed that the position of theoccupant can be used as a filter to determine the quality of the data ina particular vector. This technique can also be used in general as amethod to improve the quality of a vector of data based on the previouspositions of the occupant. This technique can also be expanded to helpdifferentiate live objects in the vehicle from inanimate objects. Forexample, a forward facing human will change his position frequentlyduring the travel of the vehicle whereas a box will tend to showconsiderably less motion. This is also useful, for example, indifferentiating a small human from an empty seat. The motion of a seatcontaining a small human will be significantly different from that of anempty seat even though the particular vector may not show significantdifferences. That is, a vector formed from the differences from twosuccessive vectors is indicative of motion and thus of an occupant.

5. Blocked transducers. It is sometimes desirable to positively identifya blocked transducer and when such a situation is found to use adifferent neural network which has only been trained on the subset ofunblocked transducers. Such a network, since it has been trainedspecifically on three transducers, for example, will generally performmore accurately than a network which has been trained on fourtransducers with one of the transducers blocked some of the time. Once ablocked transducer has been identified the occupant can be notified ifthe condition persists for more than a reasonable time.

6. Other Basic Architectures. The back propagation neural network is avery successful general-purpose network. However, for some applications,there are other neural network architectures that can perform better. Ifit has been found, for example, that a parallel network as describedabove results in a significant improvement in the system, then, it islikely that the particular neural network architecture chosen has notbeen successful in retrieving all of the information that is present inthe data. In such a case an RCE, Stochastic, Logicon Projection, or oneof the other approximately 30 types of neural network architectures canbe tried to see if the results improve. This parallel network test,therefore, is a valuable tool for determining the degree to which thecurrent neural network is capable of using efficiently the availabledata.

7. Transducer Geometry. Another technique, which is frequently used indesigning a system for a particular vehicle, is to use a neural networkto determine the optimum mounting locations, aiming directions and fieldangles of transducers. For particularly difficult vehicles it issometimes desirable to mount a large number of ultrasonic transducers,for example, and then use the neural network to eliminate thosetransducers which are least significant. This is similar to thetechnique described above where all kinds of transducers are combinedinitially and later pruned.

8. Data quantity. Since it is very easy to take large amounts data andyet large databases require considerably longer training time for aneural network, a test of the variability of the database can be madeusing a neural network. If for example after removing half of the datain the database, the performance of a trained neural network against thevalidation database does not decrease, then the system designer suspectsthat the training database contains a large amount of redundant data.Techniques such as similarity analysis can then be used to remove datathat is virtually indistinguishable from other data. Since it isimportant to have a varied database, it is undesirable generally to haveduplicate or essentially duplicate vectors in the database since thepresence of such vectors can bias system and drive the system moretoward memorization and away from generalization.

9. Environmental factors. An evaluation can be made of the beneficialeffects of using varying environmental influences during data collectionon the accuracy of the system using neural networks along with atechnique such as design of experiments.

10. Database makeup. It is generally believed that the training databasemust be flat meaning that all of the occupancy states that the neuralnetwork must recognize must be approximately equally represented in thetraining database. Typically, the independent database has approximatelythe same makeup as the training database. The validation database, onthe other hand, typically is represented in a non-flat basis withrepresentative cases from real world experience. Since there is no needfor the validation database to be flat, it can include many of theextreme cases as well as being highly biased towards the most commoncases. This is the theory that is currently being used to determine themakeup of the various databases. The success of this theory continues tobe challenged by the addition of new cases to the validation database.When significant failures are discovered in the validation database, thetraining and independent databases are modified in an attempt to removethe failure.

11. Biasing. All seated state occupancy states are not equallyimportant. The final system must be nearly 100% accurate for forwardfacing in-position humans. Since that will comprise the majority of thereal world situations, even a small loss in accuracy here will cause theairbag to be disabled in a situation where it otherwise would beavailable to protect an occupant. A small decrease in accuracy will thusresult in a large increase in deaths and injuries. On the other hand,there are no serious consequences if the airbag is deployed occasionallywhen the seat is empty. Various techniques are used to bias the data inthe database to take this into account. One technique is to give a muchhigher value to the presence of a forward facing human during thesupervised learning process than to an empty seat. Another technique isto include more data for forward facing humans than for empty seats.This, however, can be dangerous as an unbalanced network leads to a lossof generality.

12. Screening. It is important that the loop be closed on dataacquisition. That is, the data must be checked at the time the data isacquired to the sure that it is good data. Bad data can happen becauseof electrical disturbances on the power line, sources of ultrasound suchas nearby welding equipment, or due to human error. If the data remainsin the training database, for example, then it will degrade theperformance of the network. Several methods exist for eliminating baddata. The most successful method is to take an initial quantity of data,such as 30,000 to 50,000 vectors, and create an interim network. This isnormally done anyway as an initial check on the system capabilitiesprior to engaging in an extensive data collection process. The networkcan be trained on this data and, as the real training data is acquired,the data can be tested against the neural network created on the initialdata set. Any vectors that fail are examined for reasonableness.

13. Vector normalization method. Through extensive research it has beenfound that the vector should be normalized based on all of the data inthe vector, that is have all its data values range from 0 to 1. Forparticular cases, however, it has been fond desirable to apply thenormalization process selectively, eliminating or treating differentlythe data at the early part of the data from each transducer. This isespecially the case when there is significant ringing on the transduceror cross talk when a separate send and receive transducer is used. Thereare times when other vector normalization techniques are required andthe neural network system can be used to determine the best vectornormalization technique for a particular application.

14. Feature extraction. The success of a neural network system canfrequently be aided if additional data is inputted into the network. Oneexample can be the number of 0 data points before the first peak isexperience. Alternately, the exact distance to the first peak can bedetermined prior to the sampling of the data. Other features can includethe number of peaks, the distance between the peaks, the width of thelargest peak, the normalization factor, the vector mean or standarddeviation, etc. These normalization techniques are frequently used atthe end of the adaptation process to slightly increase the accuracy ofthe system.

15. Noise. It has been frequently reported in the literature that addingnoise to the data that is provided to a neural network can improve theneural network accuracy by leading to better generalization and awayfrom memorization. However, the training of the network in the presenceof thermal gradients has been shown to substantially eliminate the needto artificially add noise to the data. Nevertheless, in some cases,improvements have been observed when random arbitrary noise of a ratherlow level is superimposed on the training data.

16. Photographic recording of the setup. After all of the data has beencollected and used to train a neural network, it is common to find asignificant number of vectors which, when analyzed by the neuralnetwork, give in a weak or wrong decision. These vectors must becarefully studied especially in comparison with adjacent vectors to seeif there is an identifiable cause for the weak or wrong decision.Perhaps the occupant was on the borderline of the keep out zone andstrayed into the keep out zone during a particular data collectionevent. For this reason is desirable to photograph each setupsimultaneous with the collection of the data. This can be done using acamera mounted in a position whereby it obtains a good view of the seatoccupancy. Sometimes several cameras are necessary to minimize theeffects of blockage by a newspaper, for example. Having the photographicrecord of the data setup is also useful when similar results areobtained when the vehicle is subjected to road testing. During roadtesting, the camera is also present and the test engineer is required toinitiate data collection whenever the system does not provide thecorrect response. The vector and the photograph of this real world testcan later be compared to similar setups in the laboratory to see whetherthere is data that was missed in deriving the matrix of vehicle setupsfor training the vehicle.

17. Automation. When collecting data in the vehicle it is desirable toautomate the motion of the vehicle seat, seatback, windows, visors etc.in this manner the positions of these items can be controlled anddistributed as desired by the system designer. This minimizes thepossibility of taking too much data at one configuration and therebyunbalancing the network.

18. Automatic setup parameter recording. To achieve an accurate dataset, the key parameters of the setup should be recorded automatically.These include the temperatures at various positions inside the vehicle,the position of the vehicle seat, and seatback, the position of theheadrest, visor and windows and, where possible, the position of thevehicle occupants. The automatic recordation of these parametersminimizes the effects of human errors.

19. Laser Pointers. During the initial data collection with full hornsmounted on the surface of the passenger compartment, care must theexercised so that the transducers are not accidentally moved during thedata collection process. In order to check for this possibility, a smalllaser diode is incorporated into each transducer holder. The laser isaimed so that it illuminates some other surface of the passengercompartment at a known location. Prior to each data taking session, eachof the transducer aiming points is checked.

20. Multi-frequency transducer placement. When data is collected fordynamic out-of-position, each of the ultrasonic transducers must operateat the different frequency so that all transducers can transmitsimultaneously. By this method data can be collected every 10milliseconds, which is sufficiently fast to approximately track themotion of an occupant during pre-crash braking prior to an impact. Aproblem arises in the spacing of the frequencies between the differenttransducers. If the spacing is too close, it becomes very difficult toseparate the signals from different transducers and it also affects thesampling rate of the transducer data and thus the resolution of thetransducers. If an ultrasonic transducer operates module below 35 kHz itcan be sensed by dogs and other animals. If the transducer operates muchabove 70 kHz, it is very difficult to make the open type of ultrasonictransducer which produces the highest sound pressure. If the multiplefrequency system is used for both the driver and passenger-side, eightseparate frequencies are required. In order to find eight frequenciesbetween 35 and 70 kHz, a frequency spacing of 5 kHz is required. Inorder to use conventional electronic filters and to provide sufficientspacing to permit the desired resolution at the keep out zone border, a10 kHz spacing is desired. These incompatible requirements can be solvedthrough a careful judicious placement of the transducers such thattransducers that are within 5 kHz of each other are placed in such amanner that there is no direct path between the transducers and anyindirect path is sufficiently long so that it can be filteredtemporarily. An example of such an arrangement is shown on FIG. 11. Forthis example the transducers operate at the following frequencies A 65kHz, B 55 kHz, C 35 kHz, D 45 kHz, E 50 kHz, F 40 kHz, G 60 kHz, H 70kHz. Actually other arrangements adhering to the principle describedabove would also work.

21. Use of a PC in data collection. When collecting data for thetraining, independent, and validation databases, it is frequentlydesirable to test the data using various screening techniques and todisplay the data on a monitor. Thus, during data collection the processis usually monitored using a desktop PC for data taken in the laboratoryand a laptop PC for data taken on the road.

22. Use of referencing markers and gages. In addition to and sometimesas a substitution for, the automatic recording of the positions of theseats, seatbacks, windows etc. as described above, a variety of visualmarkings and gages are frequently used. This includes markings to showthe angular position of the seatback, the location of the seat on theseat track, the openness of the window, etc. Also in those cases whereautomatic tracking of the occupant is not implemented, visual markingsare placed such that a technician can observe that the test occupantremains within the required zone for the particular data takingexercise. Sometimes, a laser diode is used to create a visual line inthe space that represents the boundary of the keep out zone or otherdesired zone boundary.

It is important to realize to the adaptation process described hereinapplies to any combination of transducers that provide information aboutthe vehicle occupancy. These include weight sensors, capacitive sensors,inductive sensors, moisture sensors, ultrasonic, optic, infrared, radaramong others. The adaptation process begins with a selection ofcandidate transducers for a particular vehicle model. This selection isbased on such considerations as cost, alternate uses of the system otherthan occupant sensing, vehicle interior passenger compartment geometry,desired accuracy and reliability, vehicle aesthetics, vehiclemanufacturer preferences, and others. Once a candidate set oftransducers has been chosen, these transducers are mounted in the testvehicle according to the teachings of this invention. The vehicle isthen subjected to an extensive data collection process wherein variousobjects are placed in the vehicle that various locations as describedbelow and an initial data set is collected. A pattern recognition systemis then developed using the acquired data and an accuracy assessment ismade. Further studies are made to determine which if any of thetransducers can be eliminated from the design. In general the designprocess begins with a surplus of sensors plus an objective as to howmany sensors are to be in the final vehicle installation. The adaptationprocess can determine which of the transducers are most important andwhich are least important and the least important transducers can beeliminated to reduce system cost and complexity.

Although several preferred methods are illustrated and described above,there are other possible combinations using different sensors located atdifferent positions within the automobile passenger compartment whichmeasure either the same or different characteristics of an occupyingobject to accomplish the same or similar goals as those describedherein. There are also numerous additional applications in addition tothose described above including, but not limited to, monitoring thedriver seat, the center seat or the rear seat of the vehicle or forcontrolling other vehicle systems in addition to the airbag system. Thisinvention is not limited to the above embodiments and should bedetermined by the following claims.

Appendix 1

APPENDIX 1 Subject Classification Class Instances Weight Category StateES Empty Seat <10 lb Empty FFA Normally Seated Adult >105 lb Enable FFCNormally Seated Child <10, 105> lb Enable FFC Normally PositionedForward Facing Child Seat <10, 45> lb Enable OOP Out-of-positionAdult >105 lb Disable OOP Out-of-position Child <105 lb Disable OOPOut-of-position Forward Facing Child Seat <10, 45> lb Disable RFSRearward Facing Child Seat <10, 45> lb Disable RFS Rearward FacingInfant Seat <10, 45> lb Disable

Categorization of Human Subjects Weight Range Height Range kg (lb) m(in) Child <0.95, 1.15> (<3′1″, 3″9″>) <1.10, 1.30> (<3′7″, 4′3″>)<1.25, 1.45> (<4′1″, 4′9″>) <11, 25> (<24, 55>) C11 C12 C13 <22, 36>(<48, 79>) C21 C22 C23 <33, 47> (<73, 103>) C31 C32 C33 Adult <1.45,1.65> (<4′9″, 5′5″>) <1.60, 1.80> (<5′3″, 5′11″>) <1.75, 1.95> (<5′9″,6′5″>) <45, 70> (<99, 154> A11 A12 A13 <65, 90> (<143, 198>) A21 A22 A23<85, 110> (<187, 242>) A31 A32 A33

All Human Subjects are to wear light clothes (typically slacks andT-shirt) on entry. Other types of clothing to be provided by ATI

Child Surrogates Doll Baby = 0.50 m (approx. 20″) Infant = 0.75 m(approx. 30″) Child = 1.20 m (approx. 48″)

Rearward Facing Infant Seats Designation Child Seat Attributes TrainingArriva base, hood Independent Assura 565 hood Training Baby-Safe —Training Century 590 base, hood Training Evenflo Discovery base, TbarTraining Evenflo Joyride (new) hood Independent Evenflo Joyride (old) —Training Gerry Guard base Validation Kolcraft Travelabout base, TbarTraining Rock-n-Ride — Training TLC —

Rearward Facing Child Seat Designation Child Seat Attributes TrainingCentury 1000 — Validation Century 2000 STE — Training Century Ovation —Training Century Smartmove table 5T Training Champion table TrainingFisher Price Child Seat table Training Touriva — Trianing Ultara tableTraining Vario Exclusive table

Forward Facing Child and Booster Seats Designation Child Seat AttributesTraining Century 1000 — Validation Century 2000 STE — Training CenturyOvation — Validation Century Smartmove table 5T Training Champion tableValidation Fisher Price Booster — Training Fisher Price Child Seat tableTraining Gerry Booster table Training Touriva — Training Ultara tableTraining Vario Exclusive table

Vehicle Configuration Series Seat Track (+/− 0.5″) Seatback Recline (+/−2″) Windows Configuration  1  2  3  4  5  6  7  8  9  10 1 2 3 4 5 6 7 89 10 1 2 3 4 5 6 7 8 9 10 A 0  0  2  2  2  4  4  6  6  6 0 18 4 12 20 220 0 8 16 D D U U D D U U D D B 1  1  3  3  3  5  5  7  7  7 2 20 0 8 160 18 4 12 20 U U D D U U D D U U C 0  0  2  2  4  4  4  6  6  6 5 15 416 0 15 20 2 10 18 U U D D U U D D U U D 1  1  3  3  5  5  5  7  7  7 416 5 15 2 10 16 0 15 20 D D U U D D U U D D E 0  0  0  2  2  2  4  4  6 6 0 8 16 4 12 20 2 20 0 18 D D U U D D U U D D F 1  1  1  3  3  3  5  5 7  7 4 12 20 0 8 18 0 18 2 20 U U D D U U D D U U G 0  0  2  2  2  4  4 4  6  6 4 16 2 20 2 10 18 0 15 20 U U D D U U D D U U H 1  1  3  3  3 5  5  5  7  7 2 20 4 18 0 15 20 2 10 18 D D U U D D U U D D VisorConvertible Top Configuration 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10A U D U D S U D U D S U U U U U D D D D D B D U D U S D U D U S D D D DD U U U U U C U D S U D U D S U D D D D D D U U U U U D D U S D U D U SD U U U U U U D D D D D E D U D S U D U S D U U U U U U D D D D D F U DU S D U D S U D D D D D D U U U U U G U S D U D U D U S D D D D D D U UU U U H D S U D U D U D S U U U U U U D D D D D Sequence for Child SeatTraining Data Collection: Start object in center of the seat. Trainerhas both hands on the sterring wheel; With a smooth motion, push theobject fully outboard, then pull it fully inboard, then push it tocenter position, then put hands back on the steering wheel; With asmooth motion, rotate the object 45 degrees outboard, the rotate 45degrees inboard, then rotate back to center, then put hands back on thewheel Sequence for Out-of-Position Forward Facing Child Seat TrainingData Collection Start with object in the center line, leaning onto theInstrument Panel; With a smooth motion, push the object fully outboard,then pull it fully inboard, then push it to the center; Repeat thissequence with a 150 mm (6″) gap between the object and the InstrumentPanel; Apply small (+/− 10°) rotations Repeat this sequence with a 300mm (12″) gap between the object and the Instrument Panel; Apply small(+/− 10°) rotations. Sequence for Human Subject Training DataCollection. Lean forward and outboard such that head and/or shoulderstouch the Fire line; Gently traverse inboard while carefully followingthe Fire line until the center of the vehicle is reached; Lean halfwayback towards the seatback and traverse outboard up against the sidewindow. Rotate torso while doing so; Lean back into the seat andtraverse inboard towards the center. Rotate torso while doing so; Sitback in the seat; “operate” radio controls, glove box, window, or seatcontrols; assume a brace posture; Do not cross the Fire line with headand/or shoulders at any time. Sequence for Out-of-Position Human SubjectTraining Data Collection: Lean forward and outboard such that headand/or shoulders touch the Instrument Panel; Gently traverse inboardtowards the center console; Move back 150 mm (6″) and gently traverseback to the most outboard position; Move back 300 mm (12″) and gentlytraverse back to the center console, “Operate” radio controls andglovebox while head and/or shoulders remain in front of the Fire line.

Network Training Set Collection Matrix (Vehicle E) Rev 1.1 # ClassSubject/Object Attributes Actions Config. Belt Conditions 1 ES None NoneMotions of track and (A) N.A. Ambient recline 2 FFA A22 Medium Clothes,Motions in safe seating B Yes Ambient Magazine area 3 OOP A22 MediumClothes Motions in NFZ C No Ambient 4 FFC Century 1000 Infant DollMotions in safe seating D No Ambient area 5 RFS Century 1000 Baby DollMotions in entire E No Ambient seating area 6 ES None Beaded CoverMotions of track and (F) N.A. Ambient recline 7 FFA A11 Medium ClothesMotions in safe seating G Yes Ambient area 8 OOP Touriva Infant Doll,Blanket Motions in NFZ H No Ambient 9 FFC Touriva Infant Doll, BlanketMotions in safe seating A No Ambient area 10 RFS Century 590 Baby Doll,Hood Motions in entire B No Ambient seating area 11 ES None Fabric CoverMotions of track and (C) N.A. Ambient recline 12 FFA A33 Medium Clothes,Motions in safe seating D No Ambient Newspaper area 13 OOP A33 MediumClothes Motions in NFZ E Yes Ambient 14 FFC C22 Medium Clothes Motionsin safe seating F No Ambient area 15 RFS Touriva Baby Doll, BlanketMotions in entire G No Ambient seating area 16 ES None Blanket Motionsof track and (H) N.A. Ambient recline 17 FFA A21 Heavy Clothes Motionsin safe seating A No Ambient area 18 OOP C11 Heavy Clothes Motions inNFZ B No Ambient (standing) 19 FFC C11 Heavy Clothes Motions in safeseating C No Ambient area 20 RFS TLC Baby Doll Motions in entire D NoAmbient seating area 21 ES None None Motions of track and (E) N.A. SolarHeat recline 22 FFA A12 Light Clothes, Motions in safe seating F YesSolar Heat Magazine area 23 OOP A12 Light Clothes Motions in NFZ G NoSolar Heat 24 FFC Champion Infant Doll Motions in safe seating H NoSolar Heat area 25 RFS Champion Baby Doll Motions in entire A No SolarHeat seating area 26 ES None Beaded Cover Motions of track and (B) N.A.Solar Heat recline 27 FFA A23 Light Clothes Motions in safe seating CYes Solar Heat area 28 OOP Vario Exclusive Child Doll Motions in NFZ DNo Solar Heat 29 FFC Vario Exclusive Child Doll, Blanket Motions in safeseating E No Solar Heat area 30 RFS Joyride (new) Baby Doll Motions inentire F No Solar Heat seating area 31 ES None Fabric Cover Motions oftrack and (G) N.A. Solar Heat recline 32 FFA A32 Light Clothes, Motionsin safe seating H No Solar Heat Newspaper area 33 OOP A32 Light ClothesMotions in NFZ A Yes Solar Heat 34 FFC C33 Light Clothes Motions in safeseating B No Solar Heat area 35 RFS Ultara Baby Doll, Blanket Motions inentire C No Solar Heat seating area 36 ES None Blanket Motions of trackand (D) N.A. Solar Heat recline 37 FFA A22 Medium Clothes Motions insafe seating E No Solar Heat area 38 OOP C21 Medium Clothes Motions inNFZ F No Solar Heat 39 FFC C21 Medium Clothes Motions in safe seating GNo Solar Heat area 40 RFS Arriva Baby Doll, Hood Motions in entire H NoSolar Heat seating area 41 ES None Handbag Motions of track and (H) N.A.Ambient recline 42 FFA A11 Heavy Clothes, Motions in safe seating G YesAmbient Magazine area 43 OOP A11 Heavy Clothes Motions in NFZ F NoAmbient 44 FFC Gerry Booster Infant Doll Motions in safe seating E NoAmbient area 45 RFS Fisher Price CS Baby Doll Motions in entire D NoAmbient seating area 46 ES None Beaded Cover, Handbag Motions of trackand (C) N.A. Ambient recline 47 FFA A33 Heavy Clothes Motions in safeseating B Yes Ambient area 48 OOP Ultara Inflant Doll, Blanket Motionsin NFZ A No Ambient 49 FFC Ultara Inflant Doll, Blanket Motions in safeseating H No Ambient area 50 RFS Baby Safe Baby Doll, Handle up Motionsin entire G No Ambient seating area 51 ES None Fabric Cover, HandbagMotions of track and (F) N.A. Ambient recline 52 FFA A21 Heavy Clothes,Motions in safe seating E No Ambient Newspaper area 53 OOP A21 HeavyClothes Motions in NFZ D Yes Ambient 54 FFC C12 Heavy Clothes Motions insafe seating C No Ambient area 55 RFS Vario Exclusive Baby Doll, BlanketMotions in entire B No Ambient seating area 56 ES None Blanket, HandbagMotions of track and (A) N.A. Ambient recline 57 FFA A12 Rain ClothesMotions in safe seating H No Ambient area 58 OOP C23 Rain ClothesMotions in NFZ G No Ambient 59 FFC C23 Rain Clothes Motions in safeseating F No Ambient area 60 RFS Rock'n'Ride Baby Doll Motions in entireE No Ambient seating area 61 ES None None Motions of track and (D) N.A.Air Conditioner recline 62 FFA A23 Light Clothes, Motions in safeseating C Yes Air Conditioner Magazine area 63 OOP A23 Light ClothesMotions in NFZ B No Air Conditioner 64 FFC Century Inflant Doll Motionsin safe seating A No Air Conditioner Ovation area 65 RFS Century BabyDoll Motions in entire H No Air Conditioner Ovation seating area 66 ESNone Beaded Cover Motions of track and (G) N.A. Air Conditioner recline67 FFA A32 Light Clothes Motions in safe seating F Yes Air Conditionerarea 68 OOP Fisher Price CS Child Doll Motions in NFZ E No AirConditioner 69 FFC Fisher Price CS Child Doll, Blanket Motions in safeseating D No Air Conditioner area 70 RFS Gerry Guard Baby Doll Motionsin entire C No Air Conditioner seating area 71 ES None Fabric CoverMotions of track and (B) N.A. Air Conditioner recline 72 FFA A22 LightClothes, Motions in safe seating A No Air Conditioner Newspaper area 73OOP A22 Light Clothes Motions in NFZ H Yes Air Conditioner 74 FFC C32Light Clothes Motions in safe seating G No Air Conditioner area 75 RFSSmartmove 5T Baby Doll, Blanket Motions in entire F No Air Conditionerseating area 76 ES None Blanket Motions of track and (E) N.A. AirConditioner recline 77 FFA A11 Medium Clothes Motions in safe seating DNo Air Conditioner area 78 OOP C22 Medium Clothes Motions in NFZ C NoAir Conditioner 79 FFC C22 Medium Clothes Motions in safe seating B NoAir Conditioner area 80 RFS Discovery Baby Doll, Handle up Motions inentire A No Air Conditioner seating area 81 ES None Pizza Box Motions oftrack and (B) N.A. Ambient recline 82 FFA A33 Rain Clothes, MagazineMotions in safe seating A Yes Ambient area 83 OOP A33 Rain ClothesMotions in NFZ D Yes Ambient 84 FFC Champion Infant Doll Motions in safeseating C No Ambient area 85 RFS Champion Baby Doll Motions in entire FNo Ambient seating area 86 ES None Beaded Cover, Pizza Motions of trackand (E) N.A. Ambient Box recline 87 FFA A21 Rain Clothes Motions in safeseating H Yes Ambient area 88 OOP Vario Exclusive Child Doll, BlanketMotions in NFZ G No Ambient 89 FFC Vario Exclusive Child Doll, BlanketMotions in safe seating B No Ambient area 90 RFS Joyride (new) BabyDoll, Hood Motions in entire A No Ambient seating area 91 ES None FabricCover, Pizza Box Motions of track and (D) N.A. Ambient recline 92 FFAA12 Rain Clothes, Motions in safe seating C No Ambient Newspaper area 93OOP A12 Rain Clothes Motions in NFZ F No Ambient 94 FFC C23 Rain ClothesMotions in safe seating E No Ambient area 95 RFS Ultara Baby Doll,Blanket Motions in entire H No Ambient seating area 96 ES None Blanket,Pizza Box Motions of track and (G) N.A. Ambient recline 97 FFA A23 LightClothes Motions in safe seating B No Ambient area 98 OOP C32 LightClothes Motions in NFZ A No Ambient 99 FFC C32 Light Clothes Motions insafe seating D No Ambient area 100 RFS Arriva Baby Doll, Hood Motions inentire C No Ambient seating area 101 ES None None Motions of track and(F) N.A. Car Heat recline 102 FFA A32 Light Clothes, Motions in safeseating E Yes Car Heat Magazine area 103 OOP A32 Light Clothes Motionsin NFZ H Yes Car Heat 104 FFC Century 1000 Infant Doll Motions in safeseating G No Car Heat area 105 RFS Century 1000 Baby Doll Motions inentire B No Car Heat seating area 106 ES None Beaded Cover Motions oftrack and (A) N.A. Car Heat recline 107 FFA A22 Rain Clothes Motions insafe seating D Yes Car Heat area 108 OOP Vario Exclusive Inflant DollMotions in NFZ C No Car Heat 109 FFC Touriva Inflant Doll, BlanketMotions in safe seating F No Car Heat area 110 RFS Century 590 Baby DollMotions in entire E No Car Heat seating area 111 ES None Fabric CoverMotions of track and (H) N.A. Car Heat recline 112 FFA A11 LightClothes, Motions in safe seating G No Car Heat Newspaper area 113 OOPA11 Light Clothes Motions in NFZ B No Car Heat 114 FFC C32 Light ClothesMotions in safe seating A No Car Heat area 115 RFS Touriva Baby Doll,Blanket Motions in entire D No Car Heat seating area 116 ES None BlanketMotions of track and (C) N.A. Car Heat recline 117 FFA A33 Heavy ClothesMotions in safe seating F No Car Heat area 118 OOP C22 Heavy ClothesMotions in NFZ E No Car Heat 119 FFC C22 Heavy Clothes Motions in safeseating H No Car Heat area 120 RFS TLC Baby Doll Motions in entire G NoCar Heat seating area 121 ES None Attaché Case (flat) Motions of trackand (G) N.A. Ambient recline 122 FFA A21 Heavy Clothes, Motions in safeseating H Yes Ambient Magazine area 123 OOP A21 Heavy Clothes Motions inNFZ E Yes Ambient 124 FFC Century Infant Doll Motions in safe seating FNo Ambient Ovation area 125 RFS Century Baby Doll Motions in entire C NoAmbient Ovation seating area 126 ES None Beaded Cover, Attaché Motionsof track and (D) N.A. Ambient Case recline 127 FFA A12 Rain ClothesMotions in safe seating A Yes Ambient area 128 OOP Fisher Price CSInflant Doll, Blanket Motions in NFZ B No Ambient 129 FFC Fisher PriceCS Inflant Doll Motions in safe seating G No Ambient area 130 RFS GerryGuard Baby Doll, Handle up Motions in entire H No Ambient seating area131 ES None Fabric Cover, Attaché Motions of track and (E) N.A. AmbientCase recline 132 FFA A23 Heavy Clothes, Motions in safe seating F NoAmbient Newspaper area 133 OOP A23 Heavy Clothes Motions in NFZ C NoAmbient 134 FFC C11 Heavy Clothes Motions in safe seating D No Ambientarea 135 RFS Smartmove 5T Baby Doll, Blanket Motions in entire A NoAmbient seating area 136 ES None Blanket, Attaché Case Motions of trackand (B) N.A. Ambient recline 137 FFA A32 Rain Clothes Motions in safeseating G No Ambient area 138 OOP C33 Rain Clothes Motions in NFZ H NoAmbient 139 FFC C33 Rain Clothes Motions in safe seating E No Ambientarea 140 RFS Discovery Baby Doll, Handle up Motions in entire F NoAmbient seating area 141 ES None Hand Bag Motions of track and (C) N.A.Solar Heat recline 142 FFA A22 Medium Clothes, Motions in safe seating DYes Solar Heat Magazine area 143 OOP A22 Heavy Clothes Motions in NFZ AYes Solar Heat 144 FFC Gerry Booster Child Doll Motions in safe seatingB No Solar Heat area 145 RFS Fisher Price CS Baby Doll Motions in entireG No Solar Heat seating area 146 ES None Beaded Cover, Hand Motions oftrack and (H) N.A. Solar Heat Bag recline 147 FFA A11 Medium ClothesMotions in safe seating E Yes Solar Heat area 148 OOP Vario ExclusiveInflant Doll Motions in NFZ F No Solar Heat 149 FFC Ultara Inflant Doll,Blanket Motions in safe seating C No Solar Heat area 150 RFS Baby SafeBaby Doll Motions in entire D No Solar Heat seating area 151 ES NoneFabric Cover, Hand Bag Motions of track and (A) N.A. Solar Heat recline152 FFA A33 Medium Clothes, Motions in safe seating B No Solar HeatNewspaper area 153 OOP A33 Medium Clothes Motions in NFZ G No Solar Heat154 FFC C33 Medium Clothes Motions in safe seating H No Solar Heat area155 RFS Vario Exclusive Baby Doll, Blanket Motions in entire E No SolarHeat seating area 156 ES None Blanket, Hand Bag Motions of track and (F)N.A. Solar Heat recline 157 FFA A21 Light Clothes Motions in safeseating C No Solar Heat area 158 OOP C21 Light Clothes Motions in NFZ DNo Solar Heat 159 FFC C21 Light Clothes Motions in safe seating A NoSolar Heat area 160 RFS Rock'n'Ride Baby Doll Motions in entire B NoSolar Heat seating area

Network Independent Test Set Collection Matrix (Vehicle E) Rev 1.1(Under Construction) # Class Subject/Object Attributes Actions Config.Belt Conditions 1 ES Motions of track and (A) N.A. Ambient recline 2 FFAMotions in safe seating B Yes Ambient area 3 OOP Motions in NFZ C NoAmbient 4 FFC Motions in safe seating D No Ambient area 5 RFS Motions inentire E No Ambient seating area 6 ES Motions of track and (F) N.A.Ambient recline 7 FFA Motions in safe seating G Yes Ambient area 8 OOPMotions in NFZ H No Ambient 9 FFC Motions in safe seating A No Ambientarea 10 RFS Motions in entire B No Ambient seating area 11 ES Motions oftrack and (C) N.A. Ambient recline 12 FFA Motions in safe seating D NoAmbient area 13 OOP Motions in NFZ E Yes Ambient 14 FFC Motions in safeseating F No Ambient area 15 RFS Motions in entire G No Ambient seatingarea 16 ES Motions of track and (H) N.A. Ambient recline 17 FFA Motionsin safe seating A No Ambient area 18 OOP MOtions in NFZ B No Ambient(standing) 19 FFC Motions in safe seating C No Ambient area 20 RFSMotions in entire D No Ambient seating area

Appendix 2 Analysis of Neural Network Training and Data PreprocessingMethods—An Example

1. Introduction

The Artificial Neural Network that forms the “brains” of the OccupantSpatial Sensor needs to be trained to recognize airbag enable anddisable patterns. The most important part of this training is the datathat is collected in the vehicle, which provides the patternscorresponding to these respective configurations. Manipulation of thisdata (such as filtering) is appropriate if this enhances the informationcontained in the data. Important too, arc the basic network architectureand training methods applied, as these two determine the learning andgeneralization capabilities of the neural network. The ultimate test forall methods and filters is their effect on the network performanceagainst real world situations.

The Occupant Spatial Sensor (OSS) uses an artificial neural network(ANN) to recognize patterns that it has been trained to identify aseither airbag enable or airbag disable conditions. The pattern isobtained from four ultrasonic transducers that cover the front passengerseating area. This pattern consists of the ultrasonic echoes from theobjects in the passenger seat area. The signal from each of the fourtransducers consists of the electrical image of the return echoes, whichis processed by the electronics. The electronic processing comprisesamplification (logarithmic compression), rectification, and demodulation(band pass filtering), followed by discretization (sampling) anddigitization of the signal. The only software processing required,before this signal can be fed into the artificial neural network, isnormalization (i.e. mapping the input to numbers between 0 and 1).Although this is a fair amount of processing, the resulting signal isstill considered “raw”, because all information is treated equally.

It is possible to apply one or more software preprocessing filters tothe raw signal before it is fed into the artificial neural network. Thepurpose of such filters is to enhance the useful information going intothe ANN, in order to increase the system performance. This documentdescribes several preprocessing filters that were applied to the ANNtraining of a particular vehicle.

2. Data Description

The performance of the artificial neural network is dependent on thedata that is used to train the network. The amount of data and thedistribution of the data within the realm of possibilities are known tohave a large effect on the ability of the network to recognize patternsand to generalize. Data for the OSS is made up of vectors. Each vectoris a combination of the useful parts of the signals collected from fourultrasonic transducers. A typical vector could comprise on the order of100 data points, each representing the (time displaced) echo level asrecorded by the ultrasonic transducers.

Three different sets of data are collected. The first set, the trainingdata, contains the patterns that the ANN is being trained on torecognize as either an airbag deploy or non-deploy scenario. The secondset is the independent test data. This set is used during the networktraining to direct the optimization of the network weights. The thirdset is the validation (or real world) data. This set is used to quantifythe success rate (or performance) of the finalized artificial neuralnetwork.

Table 1 shows the main characteristics of these three data sets, ascollected for the vehicle. Three numbers characterize the sets. Thenumber of configurations characterizes how many different subjects andobjects were used. The number of setups is the product of the number ofconfigurations and the number of vehicle interior variations (seatposition and recline, roof and window state, etc.) performed for eachconfiguration. The total number of vectors is then made up of theproduct of the number of setups and the number of patterns collectedwhile the subject or object moves within the passenger volume.

TABLE 1 Characteristics of the Data Sets Data Set Configurations SetupsVectors Training 130 1300 650,000 Independent Test 130 1300 195,000Validation 100 100 15,000

1.1 Training Data Set Characteristics

The training data set can be split up in various ways into subsets thatshow the distribution of the data. Table 2 shows the distribution of thetraining set amongst three classes of passenger seat occupancy: EmptySeat, Human Occupant, and Child Seat. All human occupants were adults ofvarious sizes. No children were part of the training data set other thenthose seated in Forward Facing Child Seats. Table 3 shows a furtherbreakup of the Child Seats into Forward Facing Child Seats, RearwardFacing Child Seats, Rearward Facing Infant Seats, and out-of-positionForward Facing Child Seats. Table 4 shows a different type ofdistribution; one based on the environmental conditions inside thevehicle.

TABLE 2 Distribution of Main Training Subjects Occupancy RepresentationEmpty Seat 10% Human Occupant 32% Child Seat 58%

TABLE 3 Child Seat Distribution Child Seat Configuration RepresentationForward Facing Child Seat 40% Forward Facing Child Seat Out-of-Position 4% Rearward Facing Child Seat 27% Rearward Facing Infant Seat 29%

TABLE 4 Distribution of Environmental Conditions Environmental ConditionRepresentation Ambient 56% Static Heat (Solar Lamp) 25% Dynamic Heat(Car Heat) 13% Dynamic Cooling (Car A C)  6%

1.2 Independent Test Data Characteristics

The independent test data is created using the same configurations,subjects, objects, and conditions as used for the training data set. Itsmakeup and distributions are therefore the same as those of the trainingdata set.

1.3 Validation Data Characteristics

The distribution of the validation data set into its main subsets isshown in Table 5. This distribution is close to that of the trainingdata set. However, the human occupants comprised both children (12% oftotal) as well as adults (27% of total). Table 6 shows the distributionof human subjects. Contrary to the training and independent test datasets, data was collected on children ages 3 and 6 that were not seatedin a child restraint of any kind. Table 7 shows the distribution of thechild seats used. On the other hand, no data was collected on ForwardFacing Child Seats that were out-of-position. The child and infant seatsused in this data set are different from those used in the training andindependent test data sets. The validation data was collected withvarying environmental conditions as shown in Table 8.

TABLE 5 Validation Data Distribution Occupancy Representation Empty Seat 8% Human Occupant 39% Child Seat 53%

TABLE 6 Human Subject Distribution Human Occupant RepresentationNormally Seated Out-of-Position Child age 3 15% 50% 50% Child age 6 15%50% 50% Adult 5^(th) percentile 23% 67% 33% Female Adult 50^(th)percentile 23% 67% 33% Male Adult 95^(th) percentile 23% 67% 33% Male

TABLE 7 Child Seat Distribution Child Seat Configuration RepresentationForward Facing Child Seat 11% Forward Facing Booster Seat 11% RearwardFacing Child Seat 38% Rearward Facing Infant Seat 40%

TABLE 8 Distribution of Environmental Conditions Environmental ConditionRepresentation Ambient 63% Static Heat (Solar Lamp) 13% Dynamic Heat(Car Heat) 12% Dynamic Cooling (Car Air Conditioner) 12%

3. Network Training

The baseline network consisted of a four layer back-propagation networkwith 117 input layer nodes, 20 and 7 nodes respectively in the twohidden layers, and 1 output layer node. The input layer is made up ofinputs from four. ultrasonic transducers. These were located in thevehicle on the rear quarter panel (A), the A-pillar (B), and theover-head console (C, H). Table 9 shows the number of points, taken fromeach of these channels that make up one vector.

TABLE 9 Transducer Volume Starting Point End Point Transducer SampleTime (ms) Distance (mm) Sample Time (ms) Distance (mm) A 5 0.83 142 294.84 822 B 3 0.50 85 35 5.84 992 C 7 1.17 198 34 5.67 964 H 2 0.33 57 325.34 907

The artificial neural network is implemented using the NeuralWorksProfessional II/Plus software. The method used for training the decisionmathematical model was back-propagation with Extended Delta-Bar-Deltalearning rule and sigmoid transfer function. The Extended DBD paradigmuses past values of the gradient to infer the local curvature of theerror surface. This leads to a learning rule in which every connectionhas a different learning rate and a different momentum term, both ofwhich are automatically calculated.

The network was trained using the above-described training andindependent test data sets. An optimum (against the independent testset) was found after 3,675,000 training cycles. Each training cycle uses30 vectors (known as the epoch), randomly chosen from the 650,000available training set vectors. Table 10 shows the performance of thebaseline network.

TABLE 10 Baseline Network Performance Self Test Success Rate 95.3%Independent Test Success Rate 94.5% Validation Test Success Rate 92.7%

The network performance has been further analyzed by investigating thesuccess rates against subsets of the independent test set. The successrate against the airbag enable conditions at 94.6% is virtually equal tothat against the airbag disable conditions at 94.4%. Table 11 shows thesuccess rates for the various occupancy subsets. Table 12 shows thesuccess rates for the environmental conditions subsets. Although thedistribution of this data was not entirely balanced throughout thematrix, it can be concluded that the system performance is notsignificantly degraded by heat sources.

TABLE 11 Performance per Occupancy Subset Occupancy Independent TestEmpty Seat 96.1% Normally Seated Adult 92.1% Rearward FacingChild/Infant Seat 94.1% Forward Facing Child Seat 96.9% Out-of-PositionHuman/FFCS 93.0%

TABLE 12 Performance per Environmental Conditions Subset EnvironmentalCondition Independent Test Ambient 95.4% Long Term Heat (Lamp Heat)95.2% Sort Term Heating/Cooling (HVAC) 93.5%

3.1 Normalization

Normalization is used to scale the real world data range into a rangeacceptable for the network training. The NeuralWorks software requiresthe use of a scaling factor to bring the input data into a range of 0 to1, inclusive. Several normalization methods have been explored for theireffect on the system performance.

The real world data consists of 12 bit, digitized signals with valuesbetween 0 and 4095. FIG. 12 shows a typical raw signal. A raw vectorconsists of combined sections of four signals.

Three methods of normalization of the individual vectors have beeninvestigated:

a. Normalization using the highest and lowest value of the entire vector(baseline).

b. Normalization of the transducer channels that make up the vector,individually. This method uses the highest and lowest values of eachchannel.

c. Normalization with a fixed range ([0,4095]).

The results of the normalization study are summarized in Table 13.

TABLE 13 Normalization Study Results Normalization Method Self TestIndependent Test Validation Test a. Whole Vector (base) 95.3% 94.5%92.7% b. Per Channel 94.9% 93.8% 90.3% c. Fixed Range [0, 4095] 95.6%90.3% 88.3%

A higher performance results from normalizing across the entire vectorversus normalizing per channel. This can be explained from the fact thatthe baseline method retains the information contained in the relativestrength of the signal from one transducer compared to another. Thisinformation is lost when using the second method.

Normalization using a fixed range retains the information contained inthe relative strength of one vector compared to the next. From this itcould be expected that the performance of the network trained with fixedrange normalization would increase over that of the baseline method.However, without normalization. the input range is, as a rule, not fromzero to the maximum value (see FIG. 1). The absolute value of the dataat the input layer affects the network weight adjustment (see equations[1] and [2]). During network training, vectors with a smaller inputrange will affect the weights calculated for each processing element(neuron) differently than vectors that do span the full range.

Δw _(ij) ^([s]) =lcoef·e _(j) ^([s]) ·x _(l) ^([s−1])  [1]

e _(j) ^([s]) =x _(j) ^([s])·(1.0−x _(j) ^([s]))·Δ_(k)(e _(k) ^([s+1])·w _(kj) ^([s+1]))  [2]

Δw_(ij) ^([s]) is the change in the network weights; lcoef is thelearning coefficient; e_(j) ^([s]) is the local error at neuron j inlayer s; x_(l) ^([s]) is the current output state of neuron j in layers.

Variations in the highest and lowest values in the input layer,therefore, have a negative effect on the training of the network. Thisis reflected in a lower performance against the validation data set.

A secondary effect of normalization is that it increases the resolutionof the signal by stretching it out over the full range of 0 to 1,inclusive. As the network predominantly learns from higher peaks in thesignal, this results in better generalization capabilities and thereforein a higher performance.

It must be concluded that the effects of the fixed range of input valuesand the increased resolution resulting from the baseline normalizationmethod have a stronger effect on the network training than retaining theinformation contained in the relative vector strength.

3.2 Low Threshold Filters

Not all information contained in the raw signals can be considereduseful for network training. Low amplitude echoes are received back fromobjects on the outskirts of the ultrasonic field that should not beincluded in the training data. Moreover, low amplitude noise, fromvarious sources, is contained within the signal. This noise shows upstrongest where the signal is weak. By using a low threshold filter, thesignal to noise ratio of the vectors can be improved before they areused for network training.

Three cutoff levels were used: 5%, 10%, and 20% of the signal maximumvalue (4095). The method used, brings the values below the threshold upto the threshold level. Subsequent vector normalization (baselinemethod) stretches the signal to the fill range of [0,1].

The results of the low threshold filter study are summarized in Table14.

The performance of the networks trained with 5% and 10% threshold filteris similar to that of the baseline network. A small performancedegradation is observed for the network trained with a 20% thresholdfilter. From this it is concluded that the noise level is sufficientlylow to not affect the network training. At the same time it can beconcluded that the lower 10% of the signal can be discarded withoutaffecting the network performance. This allows the definition ofdemarcation lines on the outskirts of the ultrasonic field where thesignal is equal to 10% of the maximum field strength.

4. Network Types

The baseline network is a back-propagation type network.Back-propagation is a general-purpose network paradigm that has beensuccessfully used for prediction, classification, system modeling, andfiltering as well as many other general types of problems. Backpropagation learns by calculating an error between desired and actualoutput and propagating this error information back to each node in thenetwork. This back-propagated error is used to drive the learning ateach node. Some of the advantages of a back-propagation network are thatit attempts to minimize the global error and that it can provide a verycompact distributed representation of complex data sets. Some of thedisadvantages are its slow learning and the irregular boundaries andunexpected classification regions due to the distributed nature of thenetwork and the use of a transfer functions that is unbounded. Some ofthese disadvantages can be overcome by using a modified back-propagationmethod such as the Extended Delta-Bar-Delta paradigm. The EDBD algorithmautomatically calculates the learning rate and momentum for eachconnection in the network, which facilitates optimization of the networktraining.

Many other network architectures exist that have differentcharacteristics than the baseline network. One of these is the LogiconProjection Network. This type of network combines the advantages ofclosed boundary networks with those of open boundary networks (to whichthe back-propagation network belongs). Closed boundary networks are fastlearning because they can immediately place prototypes at the input datapoints and match all input data to these prototypes. Open boundarynetworks, on the other hand, have the capability to minimize the outputerror through gradient decent.

5. Conclusions

The baseline artificial neural network trained to a success rate of92.7% against the validation data set. This network has a four-layerback-propagation architecture and uses the Extended Delta-Bar-Deltalearning rule and sigmoid transfer function. Pre-processing comprisedvector normalization while post-processing comprised a “five consistentdecision” filter.

The objects and subjects used for the independent test data were thesame as those used for the training data. This may have negativelyaffected the network's classification generalization abilities.

The spatial distribution of the independent test data was as wide asthat of the training data. This has resulted in a network that cangeneralize across a large spatial volume. A higher performance across asmaller volume, located immediately around the peak of the normaldistribution, combined with a lower performance on the outskirts of thedistribution curve, might be preferable.

To achieve this, the distribution of the independent test set needs tobe a reflection of the normal distribution for the system (a.k.a. nativepopulation).

Modifying the pre-processing method or applying additionalpre-processing methods did not show a significant improvement of theperformance over that of the baseline network. The baselinenormalization method gave the best results as it improves the learningby keeping the input values in a fixed range and increases the signalresolution. The lower threshold study showed that the network learnsfrom the larger peaks in the echo pattern. Pre-processing techniquesshould be aimed at increasing the signal resolution to bring out thesepeaks.

A further study could be performed to investigate combining a lowerthreshold with fixed range normalization, using a range less than fullscale. This would force each vector, to include at least one point atthe lower threshold value and one value in saturation, effectivelyforcing each vector into a fixed range that can be mapped between 0 and1, inclusive. This would have the positive effects associated with thebaseline normalization, while retaining the information contained in therelative vector strength. Raw vectors points that, as a result of thescaling, would fall outside the range of 0 to 1 would then be mapped to0 and 1 respectively.

Post-processing should be used to enhance the network recognitionability with a memory function. The possibilities for such are currentlyfrustrated by the necessity of one network performing both objectclassification as well as spatial locating functions. Performing thespatial locating function requires flexibility to rapidly update thesystem status. Object classification, on the other hand, benefits fromdecision rigidity to nullify the effect of an occasional pattern that isincorrectly classified by the network.

Appendix 3 Process for Training an OPS System DOOP Network for aSpecific Vehicle

1. Define customer requirements and deliverables

1.1. Number of zones

1.2. Number of outputs

1.3. At risk zone definition

1.4. Decision definition i.e. empty seat at risk, safe seating, or notcritical and undetermined

1.5. Determine speed of DOOP decision

2. Develop PERT chart for the program

3. Determine viable locations for the transducer mounts

3.1. Manufacturability

3.2. Repeatability

3.3. Exposure (not able to damage during vehicle life)

4. Evaluate location of mount logistics

4.1. Field dimensions

4.2. Multipath reflections

4.3. Transducer Aim

4.4. Obstructions/Unwanted data

4.5. Objective of view

4.6. Primary DOOP transducers requirements

5. Develop documentation logs for the program (vehicle books)

6. Determine vehicle training variables

6.1. Seat track stops

6.2. Steering wheel stops

6.3. Seat back angles

6.4. DOOP transducer blockage during crash

6.5. Etc . . .

7. Determine and mark at risk zone in vehicle

8. Evaluate location physical impediments

8.1. Room to mount/hide transducers

8.2. Sufficient hard mounting surfaces

8.3. Obstructions

9. Develop matrix for training, independent, validation, and DOOP datasets

10. Determine necessary equipment needed for data collection

10.1. Child/booster/infant seats

10.2. Maps/razors/makeup

10.3. Etc . . .

11. Schedule sled tests for initial and final DOOP networks

12. Design test buck for DOOP

13. Design test dummy for DOOP testing

14. Purchase any necessary variables

14.1. Child/booster/infant seats

14.2. Maps/razors/makeup

14.3. Etc . . .

15. Develop automated controls of vehicle accessories

15.1. Automatic seat control for variable empty seat

15.2. Automatic seat back angle control for variable empty seat

15.3. Automatic window control for variable empty seat

15.4. Etc . . .

16. Acquire equipment to build automated controls

17. Build & install automated controls of vehicle variables

18. Install data collection aides

18.1. Thermometers

18.2. Seat track gauge

18.3. Seat angle gauge

18.4. Etc . . .

19. Install switched and fused wiring for:

19.1. Transducer pairs

19.2. Lasers

19.3. Decision Indicator Lights

19.4. System box

19.5. Monitor

19.6. Power automated control items

19.7. Thermometers, potentiometers

19.8. DOOP occupant ranging device

19.9. DOOP ranging indicator

19.10. Etc . . .

20. Write DOOP operating software for OPS system box

21. Validate DOOP operating software for OPS

22. Build OPS system control box for the vehicle with special DOOPoperating software

23. Validate & document system control box

24. Write vehicle specific DOOP data collection software (pollbin)

25. Write vehicle specific DOOP data evaluation program (picgraph)

26. Evaluate DOOP data collection software

27. Evaluate DOOP data evaluation software

28. Load DOOP data collection software on OPS system box and validate

29. Load DOOP data evaluation software on OPS system box and validate

30. Train technicians on DOOP data collection techniques and use of datacollection software

31. Design prototype mounts based on known transducer variables

32. Prototype mounts

33. Pre-build mounts

33.1. Install transducers in mounts

33.2. Optimize to eliminate crosstalk

33.3. Obtain desired field

33.4. Validate performance of DOOP requirements for mounts

34. Document mounts

34.1. Polar plots of fields

34.2. Drawings with all mount dimensions

34.3. Drawings of transducer location in the mount

35. Install mounts in the vehicle

36. Map fields in the vehicle using ATI designed apparatus andspecification

37. Map performance in the vehicle of the DOOP transducer assembly

38. Determine sensor volume

39. Document vehicle mounted transducers and fields

39.1. Mapping per ATI specification

39.2. Photographs of all fields

39.3. Drawing and dimensions of installed mounts

39.4. Document sensor volume

39.5. Drawing and dimensions of aim & field

40. Using data collection software and OPS system box collect initial 16sheets of training, independent, and validation data

41. Determine initial conditions for training the ANN

41.1. Normalization method

41.2. Training via back propagation or ?

41.3. Weights

41.4. Etc . . .

42. Pre-process data

43. Train an ANN on above data

44. Develop post processing strategy if necessary

45. Develop post processing software

46. Evaluate ANN with validation data and in vehicle analysis

47. Perform sled tests to confirm initial DOOP results

48. Document DOOP testing results and performance

49. Rework mounts and repeat steps 31 through 48 if necessary

50. Meet with customer and review program

51. Develop strategy for customer directed outputs

51.1. Develop strategy for final ANN multiple decision networks ifnecessary

51.2. Develop strategy for final ANN multiple layer networks ifnecessary

51.3. Develop strategy for DOOP layer/network

52. Design daily calibration jig

53. Build daily calibration jig

54. Develop daily calibration test

55. Document daily calibration test procedure & jig

56. Collect daily calibration tests

57. Document daily calibration test results

58. Rework vehicle data collection markings for customer directedoutputs

58.1. Multiple zone identifiers for data collection

59. Schedule subjects for all data sets

60. Train subjects for data collection procedures

61. Using DOOP data collection software and OPS system box collectinitial 16 sheets of training, independent, and validation data

62. Collect total amount of vectors deemed necessary by programdirectives, amount will vary as outputs and complexity of ANN varies

63. Determine initial conditions for training the ANN

63.1. Normalization method

63.2. Training via back propagation or ?

63.3. Weights

63.4. Etc . . .

64. Pre-process data

65. Train an ANN on above data

66. Develop post processing strategy

66.1. Weighting

66.2. Averaging

66.3. Etc . . .

67. Develop post processing software

68. Evaluate ANN with validation data

69. Perform in vehicle hole searching and analysis

70. Perform in vehicle non sled mounted DOOP tests

71. Determines need for further training or processing

72. Repeat steps 58 through 71 if necessary

73. Perform sled tests to confirm initial DOOP results

74. Document DOOP testing results and performance

75. Repeat steps 58 through 74 if necessary

76. Write summary performance report

77. Presentation of vehicle to the customer

78. Delivered an OPS equipped vehicle to the customer

We claim:
 1. A method of developing a system for determining theoccupancy state of a seat in a passenger compartment of a vehicle,comprising the steps of: mounting transducers in the vehicle; forming atleast one database comprising multiple data sets, each of the data setsrepresenting a different occupancy state of the seat and being formed byreceiving data from the transducers while the seat is in that occupancystate, and processing the data received from the transducers; andcreating a first, trained pattern recognition algorithm from the atleast one database capable of producing an output indicative of theoccupancy state of the seat upon inputting a data set representing anoccupancy state of the seat.
 2. The method of claim 1, wherein said stepof creating a first, trained pattern recognition algorithm from the atleast one database comprises the steps of: inputting the database intoan algorithm generating program, and running the algorithm-generatingprogram to produce the first algorithm.
 3. The method of claim 2,wherein the algorithm generating program is run to generate a neuralnetwork algorithm.
 4. The method of claim 3, further comprising the stepof: utilizing the back propagation method when generating the neuralnetwork algorithm.
 5. The method of claim 2, wherein the algorithmgenerating program uses at least one computational intelligence system.6. The method of claim 2, further comprising the steps of:pre-processing the data sets based on a set of rules derived from thedatabase and which eliminate some of the data sets from being processedby the algorithm-generating program.
 7. The method of claim 6, furthercomprising the step of: deriving the rules using the principles of fuzzylogic.
 8. The method of claim 6, further comprising the step of:utilizing the data sets eliminated from input into thealgorithm-generating program to create a database that is inputted intoan algorithm-generating program to generate a second algorithm.
 9. Themethod of claim 1, wherein the at least one database comprises aplurality of databases.
 10. The method of claim 1, further comprisingthe steps of: inputting data sets into the first, trained patternrecognition algorithm to obtain a plurality of output data, and creatinga second algorithm for combining a plurality of output data to form anew output indicative of the occupancy state of the seat.
 11. The methodof claim 10, further comprising the step of: combining the plurality ofoutput data from the first, trained pattern recognition algorithm usinga low pass filter.
 12. The method of claim 1, wherein the occupancystates of the seat include occupancy of the seat by an object selectedfrom the group comprising rear facing infant seats, forward facing humanbeing, out-of-position human being, forward facing child seats and emptyseats.
 13. The method of claim 12, wherein the occupancy states of theseat include occupancy by the objects in multiple orientations.
 14. Themethod of claim 12, wherein the occupancy states of the seat includeoccupancy by the objects and at least one accessory selected from agroup comprising newspapers, books, maps, bottles, toys, hats, coats,boxes, bags and blankets.
 15. The method of claim 1, wherein the atleast one database comprises a plurality of databases, furthercomprising the step of: providing a different distribution of occupancystates for at least one of the databases.
 16. The method of claim 1,further comprising the step of: pre-processing the data prior toprocessing the data to form the data sets.
 17. The method of claim 16,wherein said pre-processing step comprises the step of using datacreated from features of the data in the data set.
 18. The method ofclaim 17, wherein the features of the data in the data set used in saidpre-processing step are selected from a group comprising thenormalization factor, the number of data points prior to a peak, thetotal number of peaks, and the mean or variance of the data set.
 19. Themethod of claim 16, wherein said pre-processing comprising the step of:mathematically transforming the data sets using one or more of the groupcomprising normalization, truncation, logarithmic transformation,sigmoid transformation, thresholding, averaging the data over time,Fourier transforms and wavelet transforms.
 20. The method of claim 16,wherein said pre-processing step comprises the step of: subtracting datain one data set from the corresponding data in another data set tocreate a third data set of differential data.
 21. The method of claim 1,further comprising the step of: subjecting the output of the algorithmto additional processing applying principles of one of fuzzy logic andneural networks.
 22. The method of claim 1, further comprising the stepsof: testing each of the data sets by a pre-processing algorithm forreasonableness, and modifying or eliminating a data set if the values ofthe data in the data set fail the reasonableness test.
 23. The method ofclaim 1, further comprising the step of: utilizing a trained neuralnetwork to eliminate data sets that contain errors.
 24. The method ofclaim 1, further comprising the step of: biasing the algorithm toward aparticular occupancy state thereby increasing the accuracy ofidentifying that occupancy state.
 25. The method of claim 1, whereinsaid processing step comprises the step of converting the analog datafrom the transducers to digital data and combining the digital data froma plurality of the transducers to form a vector comprising a string ofdata from each of the transducers, the first, trained patternrecognition algorithm being created such that upon inputting a vectorfrom a new data set, the first, trained pattern recognition algorithmwill produce an output representing the occupancy state of the vehicleseat.
 26. The method of claim 25, further comprising the step of:normalizing the vectors in the database so that all values of the datathat comprise each vector are between a maximum and a minimum.
 27. Themethod of claim 1, wherein the at least one database comprises at least50,000 data sets.
 28. The method of claim 1, further comprising the stepof: creating at least one additional algorithm from the at least onedatabase capable of producing in combination with the first, trainedpattern recognition algorithm an output indicative of the occupancystate of the seat.
 29. The method of claim 28, wherein at least one ofthe first, trained pattern recognition algorithm and the at least oneadditional algorithm identifies the category of the occupying item ofthe seat and another of the first, trained pattern recognition algorithmand the at least one additional algorithm determines the location withinthe passenger compartment of the occupying item of the seat.
 30. Themethod of claim 28, wherein at least one of the first, trained patternrecognition algorithm and the at least one additional algorithm uses aneural network trained for a large number of training cycles and atleast one other of the first, trained pattern recognition algorithm andthe at least one additional algorithm is a neural network trained for asubstantially smaller number of training cycles.
 31. The method of claim28, wherein at least one of the first, trained pattern recognitionalgorithm and the at least one additional algorithm is trained on asubset of the data in the at least one database and at least one otherof said algorithms is trained on a different subset of the data in theat least one database.
 32. The method of claim 28, wherein the data setis inputted first into one of the first, trained pattern recognitionalgorithm and the at least one additional algorithm which determineswhich of the other algorithms will further process the data set.
 33. Amethod of developing a system for determining the occupancy state of thevehicle seat in the passenger compartment of a vehicle, comprising thesteps of: forming data sets by obtaining data representative of variousoccupying objects at various positions in the passenger compartment andoperating on at least a portion of the data to reduce the magnitude ofthe largest data values in a data set relative to the smallest datavalues; and forming a database comprising multiple data sets; andcreating a trained pattern recognition algorithm from the databasecapable of producing an output indicative of the occupancy state of thevehicle seat upon inputting a data set representing an occupancy stateof the seat.
 34. The method of claim 33, wherein the step of operatingon at least a portion of the data comprises the step of using anapproximate logarithmic transformation function.
 35. A method ofdeveloping a database for use in developing a system for determining theoccupancy state of a vehicle seat, comprising the steps of: mountingtransducers in the vehicle; providing the seat with an initial occupancystate; receiving data from the transducers; processing the data from thetransducers to form a data set representative of the initial occupancystate of the vehicle seat; changing the occupancy state of the seat andrepeating the data collection process to form another data set;collecting at least 1000 data sets into a first database, eachrepresenting a different occupancy state of the seat; creating analgorithm from the first database which correctly identifies theoccupancy state of the seat for most of the data sets in the firstdatabase; testing the algorithm using a second database of data setswhich were not used in the creation of the algorithm; identifying theoccupancy states in the second database which were not correctlyidentified by the algorithm; collecting new data comprising similaroccupancy states to the incorrectly identified states; combining thisnew data with the first database; creating a new algorithm based on thecombined database; and repeating this process until the desired accuracyof the algorithm is achieved.
 36. The method of claim 35, furthercomprising the step of: creating some of the occupancy states of theseat using live human beings.
 37. The method of claim 35, furthercomprising the step of: varying the environmental conditions inside thevehicle while data is being collected.
 38. A The method of claim 37,wherein said environmental conditions varying step comprises the step ofcreating thermal gradients within the passenger compartment.
 39. Themethod of claim 35, wherein a personal computer is used in the datacollection process and where data sets are graphically displayed on themonitor of the personal computer.
 40. The method of claim 35, furthercomprising the step of: using reference markers and gages as part of asystematic method of creating a predetermined distribution of occupancystates of the vehicle.
 41. The method of claim 35, further comprisingthe step of: automatically recording the position of various complementsof the vehicle selected from the group comprising the seat, seatback,headrest, window, visor and armrest.
 42. The method of claim 35, whereinthe varying occupancy states are created by automatically moving variousvehicle complements such as the seat and seatback during the datacollection process.
 43. The method of claim 35, further comprising thestep of: automatically photographically recording at least some of theoccupancy states of the seat.
 44. The method of claim 35, furthercomprising the step of: validating proper functioning of the transducersand the data collection process by using a standard occupancy state ofthe seat and corresponding prerecorded data set, wherein a data set isperiodically taken of the standard occupancy state and compared with theprerecorded data set.
 45. The method of claim 35, wherein the algorithmcreated from the first database is a trained pattern recognitionalgorithm.
 46. A method of developing a system for determining theoccupancy state of a passenger compartment seat of a vehicle, comprisingthe steps of: mounting a plurality of ultrasonic transducers in thevehicle; receiving an analog signal from each of the transducers;processing the analog signals from the transducers to form a data setcomprising multiple data values from each transducer representative ofthe occupancy state of the vehicle, said data processing comprising thesteps of demodulation, sampling and digitizing of the transducer data tocreate a data set of digital data; forming a database comprisingmultiple data sets; and creating at least one trained patternrecognition algorithm from the database capable of producing an outputindicative of the occupancy state of the seat upon inputting a new dataset representing an occupancy state of the seat.
 47. The method of claim46, further comprising the step of: pre-processing the new data setprior to inputting into the at least one trained pattern recognitionalgorithm to remove one or more data elements at particular locations inthe data set.
 48. The method of claim 47, wherein the removed datavalues are the data values corresponding to the first data obtainedduring each data collection cycle from the transducers.
 49. The methodof claim 48, wherein the data values which are removed from the data setcorrespond to reflections from surfaces which are furthest away from anairbag module.
 50. The method of claim 46, further comprising the stepof: using a neural network to determine which data values are to beremoved from the data set.
 51. The method of claim 46, wherein theultrasonic transducers are mounted at corners of an approximate rhombuswhich surrounds the seat.
 52. The method of claim 46, wherein theultrasonic transducers are aimed such that the ultrasonic fieldsgenerated thereby cover a substantial portion of the volume surroundingthe vehicle seat.
 53. The method of claim 46, further comprising thestep of: adjusting the transducer field angles to reduce reflections offof fixed surfaces within the vehicle.
 54. The method of claim 53,wherein said field angle adjustment means utilizes horns.
 55. A methodof developing a system for determining the occupancy state of a vehicleseat in a passenger compartment of a vehicle, comprising the steps of:mounting a set of transducers on the vehicle; receiving data from thetransducers; processing the data from transducers to form a data setrepresentative of the occupancy state of the vehicle; forming a databasecomprising multiple data sets; creating an algorithm from the databasecapable of producing an output indicative of the occupancy state of thevehicle seat upon inputting a new data set; developing a measure ofsystem accuracy; removing at least one of the transducers from thetransducer set; creating a new database containing data only from thereduced number of transducers; developing a new algorithm based on thenew database; testing the new algorithm to determine the new systemaccuracy; and continuing the process of removing transducers, algorithmdevelopment and testing until the minimum number of sensors isdetermined which produces an algorithm having desired accuracy.
 56. Themethod of claim 55, wherein the transducers are selected from the groupconsisting of ultrasonic transducers, optical sensors, capacitivesensors, weight sensors, seat position sensors, seatback positionsensors, seat belt buckle sensors, seatbelt payout sensors, infraredsensors, inductive sensors and radar sensors.
 57. The method of claim55, wherein the algorithms created and developed from the databases aretrained pattern recognition algorithms.
 58. A method of developing asystem for determining the occupancy state of the driver and passengerseats of a vehicle, comprising the steps of: mounting ultrasonictransducers having different transmitting and receiving frequencies in avehicle such that transducers having adjacent frequencies are not withinthe direct ultrasonic field of each other; receiving data from thetransducers; processing the data from the transducers to form a data setrepresentative of the occupancy state of the vehicle; forming at leastone database comprising multiple data sets; and creating at least onealgorithm from the at least one database capable of producing an outputindicative of the occupancy state of a vehicle seat upon inputting a newdata set.
 59. The method of claim 58, wherein the at least one algorithmcreated from the at least one database is a trained pattern recognitionalgorithm.